Suche senden
Hochladen
Fake Video Creation and Detection: A Review
•
0 gefällt mir
•
10 views
IRJET Journal
Folgen
https://www.irjet.net/archives/V10/i3/IRJET-V10I3100.pdf
Weniger lesen
Mehr lesen
Ingenieurwesen
Melden
Teilen
Melden
Teilen
1 von 5
Jetzt herunterladen
Downloaden Sie, um offline zu lesen
Empfohlen
A Neural Network Approach to Deep-Fake Video Detection
A Neural Network Approach to Deep-Fake Video Detection
IRJET Journal
A SURVEY ON DEEPFAKES CREATION AND DETECTION
A SURVEY ON DEEPFAKES CREATION AND DETECTION
IRJET Journal
IRJET - Applications of Image and Video Deduplication: A Survey
IRJET - Applications of Image and Video Deduplication: A Survey
IRJET Journal
MINI PROJECT 2023 deepfake detection.pptx
MINI PROJECT 2023 deepfake detection.pptx
swathiravishankar3
A survey on passive digital video forgery detection techniques
A survey on passive digital video forgery detection techniques
IJECEIAES
DEEPFAKE DETECTION TECHNIQUES: A REVIEW
DEEPFAKE DETECTION TECHNIQUES: A REVIEW
vivatechijri
Recent advances in content based video copy detection (IEEE)
Recent advances in content based video copy detection (IEEE)
PACE 2.0
Video copy detection using segmentation method and
Video copy detection using segmentation method and
eSAT Publishing House
Empfohlen
A Neural Network Approach to Deep-Fake Video Detection
A Neural Network Approach to Deep-Fake Video Detection
IRJET Journal
A SURVEY ON DEEPFAKES CREATION AND DETECTION
A SURVEY ON DEEPFAKES CREATION AND DETECTION
IRJET Journal
IRJET - Applications of Image and Video Deduplication: A Survey
IRJET - Applications of Image and Video Deduplication: A Survey
IRJET Journal
MINI PROJECT 2023 deepfake detection.pptx
MINI PROJECT 2023 deepfake detection.pptx
swathiravishankar3
A survey on passive digital video forgery detection techniques
A survey on passive digital video forgery detection techniques
IJECEIAES
DEEPFAKE DETECTION TECHNIQUES: A REVIEW
DEEPFAKE DETECTION TECHNIQUES: A REVIEW
vivatechijri
Recent advances in content based video copy detection (IEEE)
Recent advances in content based video copy detection (IEEE)
PACE 2.0
Video copy detection using segmentation method and
Video copy detection using segmentation method and
eSAT Publishing House
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
IJERD Editor
A Study of Image Tampering Detection
A Study of Image Tampering Detection
IRJET Journal
Deep fake video detection using machine learning.docx
Deep fake video detection using machine learning.docx
Shakas Technologies
YU JIANGANG
YU JIANGANG
butest
Multimodel Operation for Visually1.docx
Multimodel Operation for Visually1.docx
AROCKIAJAYAIECW
Fast discrimination of fake video manipulation
Fast discrimination of fake video manipulation
IJECEIAES
IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...
IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...
IRJET Journal
Mobile Recorded Videos
Mobile Recorded Videos
IJECEIAES
Parking Surveillance Footage Summarization
Parking Surveillance Footage Summarization
IRJET Journal
Video Stabilization using Python and open CV
Video Stabilization using Python and open CV
IRJET Journal
Croma_key_Report.pdf
Croma_key_Report.pdf
GauriHadgekar
18 17 jan17 13470 rakesh ahuja revised-version(edit)
18 17 jan17 13470 rakesh ahuja revised-version(edit)
IAESIJEECS
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
IJCSEIT Journal
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Robust face recognition from multi...
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Robust face recognition from multi...
IEEEBEBTECHSTUDENTPROJECTS
SIGN LANGUAGE INTERFACE SYSTEM FOR HEARING IMPAIRED PEOPLE
SIGN LANGUAGE INTERFACE SYSTEM FOR HEARING IMPAIRED PEOPLE
IRJET Journal
IRJET- Framework for Image Forgery Detection
IRJET- Framework for Image Forgery Detection
IRJET Journal
Human Face Identification
Human Face Identification
bhupesh lahare
Video content analysis and retrieval system using video storytelling and inde...
Video content analysis and retrieval system using video storytelling and inde...
IJECEIAES
Multimodal Biometrics Recognition from Facial Video via Deep Learning
Multimodal Biometrics Recognition from Facial Video via Deep Learning
cscpconf
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNING
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNING
csandit
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
IRJET Journal
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
IRJET Journal
Weitere ähnliche Inhalte
Ähnlich wie Fake Video Creation and Detection: A Review
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
IJERD Editor
A Study of Image Tampering Detection
A Study of Image Tampering Detection
IRJET Journal
Deep fake video detection using machine learning.docx
Deep fake video detection using machine learning.docx
Shakas Technologies
YU JIANGANG
YU JIANGANG
butest
Multimodel Operation for Visually1.docx
Multimodel Operation for Visually1.docx
AROCKIAJAYAIECW
Fast discrimination of fake video manipulation
Fast discrimination of fake video manipulation
IJECEIAES
IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...
IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...
IRJET Journal
Mobile Recorded Videos
Mobile Recorded Videos
IJECEIAES
Parking Surveillance Footage Summarization
Parking Surveillance Footage Summarization
IRJET Journal
Video Stabilization using Python and open CV
Video Stabilization using Python and open CV
IRJET Journal
Croma_key_Report.pdf
Croma_key_Report.pdf
GauriHadgekar
18 17 jan17 13470 rakesh ahuja revised-version(edit)
18 17 jan17 13470 rakesh ahuja revised-version(edit)
IAESIJEECS
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
IJCSEIT Journal
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Robust face recognition from multi...
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Robust face recognition from multi...
IEEEBEBTECHSTUDENTPROJECTS
SIGN LANGUAGE INTERFACE SYSTEM FOR HEARING IMPAIRED PEOPLE
SIGN LANGUAGE INTERFACE SYSTEM FOR HEARING IMPAIRED PEOPLE
IRJET Journal
IRJET- Framework for Image Forgery Detection
IRJET- Framework for Image Forgery Detection
IRJET Journal
Human Face Identification
Human Face Identification
bhupesh lahare
Video content analysis and retrieval system using video storytelling and inde...
Video content analysis and retrieval system using video storytelling and inde...
IJECEIAES
Multimodal Biometrics Recognition from Facial Video via Deep Learning
Multimodal Biometrics Recognition from Facial Video via Deep Learning
cscpconf
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNING
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNING
csandit
Ähnlich wie Fake Video Creation and Detection: A Review
(20)
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
A Study of Image Tampering Detection
A Study of Image Tampering Detection
Deep fake video detection using machine learning.docx
Deep fake video detection using machine learning.docx
YU JIANGANG
YU JIANGANG
Multimodel Operation for Visually1.docx
Multimodel Operation for Visually1.docx
Fast discrimination of fake video manipulation
Fast discrimination of fake video manipulation
IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...
IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...
Mobile Recorded Videos
Mobile Recorded Videos
Parking Surveillance Footage Summarization
Parking Surveillance Footage Summarization
Video Stabilization using Python and open CV
Video Stabilization using Python and open CV
Croma_key_Report.pdf
Croma_key_Report.pdf
18 17 jan17 13470 rakesh ahuja revised-version(edit)
18 17 jan17 13470 rakesh ahuja revised-version(edit)
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Robust face recognition from multi...
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Robust face recognition from multi...
SIGN LANGUAGE INTERFACE SYSTEM FOR HEARING IMPAIRED PEOPLE
SIGN LANGUAGE INTERFACE SYSTEM FOR HEARING IMPAIRED PEOPLE
IRJET- Framework for Image Forgery Detection
IRJET- Framework for Image Forgery Detection
Human Face Identification
Human Face Identification
Video content analysis and retrieval system using video storytelling and inde...
Video content analysis and retrieval system using video storytelling and inde...
Multimodal Biometrics Recognition from Facial Video via Deep Learning
Multimodal Biometrics Recognition from Facial Video via Deep Learning
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNING
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNING
Mehr von IRJET Journal
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
IRJET Journal
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
IRJET Journal
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
IRJET Journal
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
IRJET Journal
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
IRJET Journal
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
IRJET Journal
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
IRJET Journal
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
IRJET Journal
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
IRJET Journal
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
IRJET Journal
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
IRJET Journal
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
IRJET Journal
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
IRJET Journal
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
IRJET Journal
React based fullstack edtech web application
React based fullstack edtech web application
IRJET Journal
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
IRJET Journal
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
IRJET Journal
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
IRJET Journal
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
IRJET Journal
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
IRJET Journal
Mehr von IRJET Journal
(20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
React based fullstack edtech web application
React based fullstack edtech web application
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Kürzlich hochgeladen
Energy Awareness training ppt for manufacturing process.pptx
Energy Awareness training ppt for manufacturing process.pptx
siddharthjain2303
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
Asst.prof M.Gokilavani
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024
Mark Billinghurst
Past, Present and Future of Generative AI
Past, Present and Future of Generative AI
abhishek36461
Work Experience-Dalton Park.pptxfvvvvvvv
Work Experience-Dalton Park.pptxfvvvvvvv
LewisJB
complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...
asadnawaz62
Industrial Safety Unit-IV workplace health and safety.ppt
Industrial Safety Unit-IV workplace health and safety.ppt
Narmatha D
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
Dr SOUNDIRARAJ N
Risk Assessment For Installation of Drainage Pipes.pdf
Risk Assessment For Installation of Drainage Pipes.pdf
ROCENODodongVILLACER
THE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTION
THE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTION
jhunlian
US Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of Action
Mebane Rash
An experimental study in using natural admixture as an alternative for chemic...
An experimental study in using natural admixture as an alternative for chemic...
Chandu841456
System Simulation and Modelling with types and Event Scheduling
System Simulation and Modelling with types and Event Scheduling
BootNeck1
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
sdickerson1
Introduction to Machine Learning Unit-3 for II MECH
Introduction to Machine Learning Unit-3 for II MECH
C Sai Kiran
National Level Hackathon Participation Certificate.pdf
National Level Hackathon Participation Certificate.pdf
RajuKanojiya4
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
jennyeacort
POWER SYSTEMS-1 Complete notes examples
POWER SYSTEMS-1 Complete notes examples
Dr. Gudipudi Nageswara Rao
Mine Environment II Lab_MI10448MI__________.pptx
Mine Environment II Lab_MI10448MI__________.pptx
Romil Mishra
Introduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptx
k795866
Kürzlich hochgeladen
(20)
Energy Awareness training ppt for manufacturing process.pptx
Energy Awareness training ppt for manufacturing process.pptx
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024
Past, Present and Future of Generative AI
Past, Present and Future of Generative AI
Work Experience-Dalton Park.pptxfvvvvvvv
Work Experience-Dalton Park.pptxfvvvvvvv
complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...
Industrial Safety Unit-IV workplace health and safety.ppt
Industrial Safety Unit-IV workplace health and safety.ppt
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
Risk Assessment For Installation of Drainage Pipes.pdf
Risk Assessment For Installation of Drainage Pipes.pdf
THE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTION
THE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTION
US Department of Education FAFSA Week of Action
US Department of Education FAFSA Week of Action
An experimental study in using natural admixture as an alternative for chemic...
An experimental study in using natural admixture as an alternative for chemic...
System Simulation and Modelling with types and Event Scheduling
System Simulation and Modelling with types and Event Scheduling
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
TechTAC® CFD Report Summary: A Comparison of Two Types of Tubing Anchor Catchers
Introduction to Machine Learning Unit-3 for II MECH
Introduction to Machine Learning Unit-3 for II MECH
National Level Hackathon Participation Certificate.pdf
National Level Hackathon Participation Certificate.pdf
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
POWER SYSTEMS-1 Complete notes examples
POWER SYSTEMS-1 Complete notes examples
Mine Environment II Lab_MI10448MI__________.pptx
Mine Environment II Lab_MI10448MI__________.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptx
Fake Video Creation and Detection: A Review
1.
© 2023, IRJET
| Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 635 Fake Video Creation and Detection: A Review Chaitali Jadhav1, Dr. Varsha Ratnaparkhe2 1Department of Electronics & Telecommunication Engineering, Government College of Engineering Aurangabad, Maharashtra. 2Associate Professor, Department of Electronics & Telecommunication Engineering, Government College of Engineering Aurangabad, Maharashtra. ----------------------------------------------------------------------***-------------------------------------------------------------------- Abstract: The improvement in fake video creation and manipulation has been accelerated by deep learning techniques. The line separating real and fake video has shrunk dramatically due to advancements in deep learning methods especially in the field of Generative Adversarial Networks (GAN). This provides access to a number of intriguing applications in various industries for example VFX creation, advertising, video games, etc. On the other hand, there is numerous disadvantages of this technique which create significant security risks such as privacy threat, Nation security, etc. Nowadays Social media platform is also the key factor in spreading these fake videos. The rapid growth of deepfake videos has an effect on people's personal, social, and political lives. Deepfakes are frequently used to produce obscene videos, harming people's reputations. Thus, there is a critical need for automated solutions that can identify a fake variety of multimedia materials and prevent the inaccurate information from being circulated. To understand how deepfakes function, several experiments have lately been conducted, and several deep learning-based methods have been developed to recognize deepfake videos. This Survey paper provide a detailed study of the methods, algorithms used to create fake Videos also the detection methods which are used to detect this fake Video. I. Introduction Digital videos are becoming a highly prevalent type of digital asset due to the rise and popularisation of social networks. Social media platforms or social network platform refers to group interactions where individuals produce, share, or exchange knowledge. Despite great benefits, Social media is used to share fake information, fake news, and fake videos. Deepfake is the term for digital media that has been altered, such as pictures or videos, in which the resemblance of another person has been substituted in lieu of that person's image or video. Deepfake has regularly been used to overlay the faces of actor/actress over obscene photographs and videos. athering the aligned faces of two different people, and training an auto-encoder are the steps in the creation of Deepfake pictures. Deep learning has changed the rules of the game, necessitating the use of multimedia audio visual forensics to find new solutions quickly. It has also been utilised to create false information and rumours for politicians [1-3]. There are many ways to identify fake images, but the majority of them either assess differences from what a typical camera pipeline would look like or extract particular picture modifications from the final image [4,5]. It has been shown that image noise [6] works well as a signal for splicing detection i.e., copy and paste mechanism from an image to another. Despite considerable advancements, there are still several fundamental issues remain that should be addressed for the purpose of improving deepfake detection algorithm. Image detection algorithms and techniques cannot be used for videos because of the substantial loss of the frames data and compression in the video [7]. This paper presents the fake video creation technique and video editing procedures as well as study tackles the issue of identifying and detection of edited video or fake videos. II. Deepfake Video Generation: The majority of these fake videos are made using deep learning techniques. The parallel training of two autoencoders is the core and central concept here. The frame work of an auto-encoder consist of the interconnection of a decoder network and an encoder network. The encoder's function is to conduct a dimension reduction by encoding the input or entry layer data into a fewer variable. The decoder’s objective is to use those factors to generate an outcome that closely resembles the original input. Deep learning technologies called Generative adversarial networks (GAN) can be used to produce phoney images or photos, movies, videos that are difficult for humans to identify from the actual thing. In this technique a data set is utilised to train models, which are subsequently used to produce phoney pictures and movies. The model has the ability to produce credible and realistic images and videos with an increase in dataset. Thus for realistic result the large amount of data must be feed to the model. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 03 | Mar 2023 www.irjet.net p-ISSN: 2395-0072
2.
© 2023, IRJET
| Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 636 Fig. (1): Generative adversarial networks (GAN) Architecture 1. Face Swapping: The Face Swapping technique is Manipulation technique. The method known as "swapping" involves a video that substitutes one person's visage with another person's [8]. A generative method must be used to process each frame of the target video in order to a produce a face swapping video. The autoencoder, which is frequently employed for data reconstruction tasks, served as the foundation for the majority of the deepfake algorithms used in face swapping. Throughout the training period, the common traits and features from source video face and target video face are extracted by two encoders. These extracted features and traits are feed to two decoders in order to reassemble the faces. To exchange and switch faces between original video frames and destination video frames, two autoencoders have been trained. The encoder first extracts latent features from the image, which are then used by the decoder to construct a replica of the original. At the end of the training phase, one of the decoders generates latent face and feed it to another decoder to reconstruct face. The Generated face will have same feature like original one if and only if the autoencoder is trained well. Previous work shows how swapping of face is performed using different method for example, In unrestricted conditions, a typical fully convolutional network was used by Nirkin et al. [9]Similarly Natsume et al. [10] presented an area that separates GAN(RSGAN) for swapping of faces. Convolution Neural Network was used by Korshunova et al. [11] to develop a face swapping method. Real time face swapping method was proposed by Wang et al. [12] For privacy protection a face was procedure was proposed by Mahajan et al. [13] 2. Face Reenactment: This technique involves changing of facial expression of an individual's face in a video [14]. In this technique the process to perform face re- enactment A monocular face reconstruction method is used to derive the low-dimensional parameter for example head position and expression of the source and target videos. Scene illumination and identification parameters are kept when performing face reenactment, but head attitude, expression, and eye gazing factors are altered. On this modified parameter the fake images of the target are regenerated. The conditional input of our new renderer video conversion network is then provided with these images. A new rendering video conversion network takes this fake image as conditional input, after that, the network is trained to transform this artificial input into a realistic output. The conditioning space time volumes are delivered to the network in a sliding window method. In order to obtain complete and better time consistency in a video. Kim et al. [15], Zhang et al. [16], Nirkin et al. [17], Doukas et al. [18], Cao et al. [19] developed a number of algorithms, including RNN, encoder decoder, GANs, a single landmark converter that includes a geometry aware generator, task agnostic GAN based schemes. Similarly RGB video feeds with real-time facial reenactments where developed by Thies et al. [20]. III. Deepfake Video Detection: In recent approaches deep learning was utilised to abruptly extricate important and discriminating properties in order to identify fakeness in video. False video detection is viewed as a conundrum of binary a binary distinction, where the distinction between genuine and manipulated videos is made using classifiers. Visual artifacts inside frames and Temporal Characteristics across Frames are the two categories into which fake video detection is divided. 1. Visual Artifacts Inside Video Frames: In this detection method the videos are broken down into frame and then visual artifacts are examined within each frame in order to obtain discriminant feature. To identify authenticity of videos, a deep or shallow classifier is fed with these features. Deep Classifier: Deepfake videos are frequently produced at low and limited resolutions, which calls for the use of an affine face warping technique that is resize, rotate, and shear the faces to match the originals appearance. Convolution Neural Network (CNN) models such as ResNet50, ResNet152 [21], VGG16 [22], and ResNet101 can International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 03 | Mar 2023 www.irjet.net p-ISSN: 2395-0072
3.
© 2023, IRJET
| Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 637 recognise the distortions left by this technique owing to the resolution discrepancy between the distorted face region and the background information. In [23] based on the relics spotted during the face warping stage of deepfake generating algorithms, there was a suggestion for a deep learning technique to recognise deepfakes. In [24] The capsule network is proposed for detection fake images and videos. The initial purpose of the capsule network was to overcome the shortcomings of CNNs when employed for inverted graphics job which seek to identify the actual 8mechanisms that produce representations of the environment [25]. Shallow classifiers: Most deepfake detection algorithms concentrate on artifacts or discrepancies between actual and false videos. In 2019 Yang et al. [26] offered an identification method based on tracking variations in 3D head positions, which are calculated utilising 68 facial areas in the essential face recognition system. Due to a flaw in the method used to create fake images, false image can be detected by 3-dimensional head positions examination. Retrieved features are feed to a SVM classification model in order to achieve high accuracy. The presence of global inconsistency and lack of an imprecise or wrong assessment of occurrence of illumination, or an improper reverence of the actual configuration are what lead to the graphical abnormalities. Deepfakes are identified using features of texture derived from the face area based on facial point of interest, missing facts and insights in the teeth and eye area, texture features. 2. Temporal Patterns Across Video Frames: In this method Deep recurrent network (RNN) models are mostly run down to identify phoney videos. In [27] Sabir et al. used the spatiotemporal properties of video streams to detect since the deepfake synthesis process does not successfully ensure temporal coherence. It is hypothesised such a modest degree distortions caused by facial alterations will surplus oneself as temporal artifacts with discrepancies among frames because video editing is done frame by frame. To take advantage of temporal differences between frames, a Recurrent Convolutional Network according to the incorporation of the convolutional network Dense Net [28] and gated recurrent unit cells [29] was developed. The long short term memory (LSTM) and CNN are used in the temporal-aware pipeline method to find deep fake videos, this technique was proposed by Guera et al [30] since there was temporal inconsistencies between frames and intra-frame inconsistencies present in fake videos. Using CNN, frame-level features are extracted, and the LSTM is then used to build a temporal sequence descriptor. In [31] Li et al. proposed the use of eye blinking as a physiological indication to identify deep fakes. A person blinks much less frequently in deepfakes than in unaltered videos. For the purpose of predicting dynamic state, Li et al. [31] crop eye regions from the videos and spread them across long term recurrent convolutional networks (LRCN). The LRCN is made up of a feature and attribute extractor based on Convolution Neural Network, a component of sequence learning based on long short term memory (LSTM), and a state prediction component based on a fully connected layer to forecast the likelihood of an eye open or closure. IV. Conclusion Deepfakes or Artificial intelligence generated or digitally altered videos, pose a serious threat to the reliability of face recognition technology and the veracity of online information. The study shows that even if cutting-edge facial image manipulation techniques provide aesthetically appealing outcomes, trained forgery detectors can still spot them. The artificial intelligence research community will be benefited from such kind of detection methods in order to create efficient strategies for combating deepfakes. To further strengthen present false detection systems, more work is required as, there are many artifacts that cannot be detected in the compressed video. Presence of extra blurriness in video creates difficulty for algorithm to detect the fake videos. Thus it is necessity for future more technique should be presented in order to deal with advancement in deep learning. Robustness is frequently used to assess how well detection algorithms work under various degradations. In the future, increasing the resilience and robustness of current detection techniques will be crucial. References: [1] Nataraj et al. (2019), “Detecting GAN Generated Fake Images Using Co-Occurrence Matrices”. Electronic Imaging, 2019, 532-1-532-7 [2] Wang et al. (2020), “CNN-Generated Images Are Surprisingly Easy to Spot... for Now”. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 03 | Mar 2023 www.irjet.net p-ISSN: 2395-0072
4.
© 2023, IRJET
| Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 638 Recognition, Seattle, 13-19 June 2020, 8695-8704. https://doi.org/10.1109/CVPR42600.2020.00872 [3] Hsu et al. (2018), “Learning to Detect Fake Face Images in the Wild”. 2018 IEEE International Symposium on Computer, Consumer and Control (IS3C), Taichung, 6- 8 December 2018, 388-391. [4] H. Farid et al (2009), “A Survey Of Image Forgery Detection”. IEEE Signal Processing Magazine, 26(2):26– 25, 2009. [5] J. A. Redi et al.(2011), “Digital image forensics: a booklet for beginners”. Multimedia Tools and Applications, 51(1):133–162, 2011. [6] T. Julliand et al. (2015), “Image noise and digital image forensics”. In Y.-Q. Shi, J. H. Kim, F. Perez-Gonz ´ alez, ´ and I. Echizen, editors, Digital-Forensics and Watermarking: 14th International Workshop (IWDW 2015), volume 9569, pages 3–17, Tokyo, Japan, October 2015. [7] Afchar et al. (2018), “MesoNet: a compact facial video forgery detection network”. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pages 1–7. IEEE, 2018. [8] Tolosana et al. (2020), “Deepfakes and beyond: A survey of face manipulation and fake detection”. Inf. Fusion 2020, 64, 131–148. [9] Nirkin et al. (2018), “On Face Segmentation, Face Swapping, and Face Perception”. In Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition, Xi’an, China, 15–19 May 2018; pp. 98–105. [10] Natsume et al. (2018), “RSGAN: Face Swapping and Editing Using Face and Hair Representation in Latent Spaces”. arXiv 2018, arXiv:1804.03447. [11] Korshunova et al. (2017), “Fast Face-Swap Using Convolutional Neural Networks”. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22–27 October 2017; pp. 3697– 3705. [12] Wang et al. (2018), “ Robust and Real-Time Face Swapping Based on Face Segmentation and CANDIDE-3”. In Proceedings of the PRICAI 2018: Trends in Artificial Intelligence, Nanjing, China, 28–31 August 2018; pp. 335–342. [13] Mahajan et al. (2017), “SwapItUp: A Face Swap Application for Privacy Protection”. In Proceedings of the IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), Taipei, Taiwan, 27–29 March 2017; pp. 46–50. [14] Juefei-Xu et al. (2022), “Countering Malicious DeepFakes: Survey, Battleground, and Horizon”. Int. J. Comput. Vis. 2022, 130, 1678–1734. [15] Kim et al.(2018), “Deep video portraits”. ACM Trans. Graph. (TOG) 2018, 37, 1–4. [16] Zhang et al. (2020), “Freenet: Multi-identity face reenactment”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 5326–5335. [17] Nirkin et al. (2019), “FSGAN: Subject agnostic face swapping and reenactment”. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 27 October–2 November 2019; pp. 7184– 7193 [18] Doukas et al. (2021), “Head2Head++: Deep Facial Attributes Re-Targeting”. IEEE Trans. Biom. Behav. Identity Sci. 2021, 3, 31–43. [19] Cao et al. (2020), “Task-agnostic Temporally Consistent Facial Video Editing”. arXiv 2020, arXiv:2007.01466 [20] Thies et al.(2016), “Face2face: Real-time face capture and reenactment of RGB videos”. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 2387–2395 [21] Kaiming et al. (2016), “Deep residual learning for image recognition”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778, 2016. [22] Karen Simonyan and Andrew Zisserman(2014). “Very deep convolutional networks for large-scale image recognition”. arXiv preprint arXiv:1409.1556, 2014. [23] Yuezun Li et al. (2018), “Exposing deepfake videos by detecting face warping artifacts”. arXiv preprint arXiv:1811.00656, 2018 [24] Nguyen et al. (2019), “Capsule forensics: Using capsule networks to detect forged images and videos”. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2307–2311. IEEE, 2019. [25] Hinton et al. (2011), “Transforming auto-encoders”. In International Conference on Artificial Neural Networks, pages 44–51. Springer, 2011. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 03 | Mar 2023 www.irjet.net p-ISSN: 2395-0072
5.
© 2023, IRJET
| Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 639 [26] Yang, X., Li, Y. and Lyu, S. (2019) Exposing Deep Fakes Using Inconsistent Head Poses. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, 12-17 May 2019, 8261- 8265. [27] Sabir et al. (2019), “Recurrent convolutional strategies for face manipulation detection in videos”. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 3(1):80–87, 2019. [28] Huang et al. (2017), “Densely connected convolutional networks”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4700–4708, 2017. [29] Cho et al. (2014), “Learning phrase representations using RNN encoder-decoder for statistical machine translation”. arXiv preprint arXiv:1406.1078, 2014 [30] Guera et al. (2018), “Deepfake video detection using recurrent neural networks”. In 15th IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS), pages 1–6. IEEE, 2018. [31] Li et al. (2018), “In ictu oculi: Exposing ai created fake videos by detecting eye blinking”. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pages 1–7. IEEE, 2018. [32] Donahue et al. (2015), “Long-term recurrent convolutional networks for visual recognition and description”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2625– 2634, 2015 [33] Nguyen et al.(2019), “Deep Learning for Deepfakes Creation and Detection: A Survey”. In Computer Vision and Image Understanding,2019. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 03 | Mar 2023 www.irjet.net p-ISSN: 2395-0072
Jetzt herunterladen