Peculiarities of the Investigation of Crimes and Administrative Offenses Committed in the Digital Environment

Sultanov, K.A.; Shevtsov, Alexander; Николаев, А Г

doi:10.2991/aebmr.k.200502.154

Cited by 1 publication

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“…The more samples, the more realistic deepfake videos are generated. The Obama video was created by projecting the video for more than 56 h for a sample recording to make it realistic ( Media Update, 2020 ; Floridi, 2018 ; Sultanov, Shevtsov & Nikolaev, 2020 ). Commonly, auto-encoders ( Ballard, 1987 ) have been used to create deep fakes.…”

Section: Methodsmentioning

confidence: 99%

Deepfake attack prevention using steganography GANs

Noreen¹,

Muneer²,

Gillani³

2022

PeerJ Computer Science

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Background Deepfakes are fake images or videos generated by deep learning algorithms. Ongoing progress in deep learning techniques like auto-encoders and generative adversarial networks (GANs) is approaching a level that makes deepfake detection ideally impossible. A deepfake is created by swapping videos, images, or audio with the target, consequently raising digital media threats over the internet. Much work has been done to detect deepfake videos through feature detection using a convolutional neural network (CNN), recurrent neural network (RNN), and spatiotemporal CNN. However, these techniques are not effective in the future due to continuous improvements in GANs. Style GANs can create fake videos with high accuracy that cannot be easily detected. Hence, deepfake prevention is the need of the hour rather than just mere detection. Methods Recently, blockchain-based ownership methods, image tags, and watermarks in video frames have been used to prevent deepfake. However, this process is not fully functional. An image frame could be faked by copying watermarks and reusing them to create a deepfake. In this research, an enhanced modified version of the steganography technique RivaGAN is used to address the issue. The proposed approach encodes watermarks into features of the video frames by training an “attention model” with the ReLU activation function to achieve a fast learning rate. Results The proposed attention-generating approach has been validated with multiple activation functions and learning rates. It achieved 99.7% accuracy in embedding watermarks into the frames of the video. After generating the attention model, the generative adversarial network has trained using DeepFaceLab 2.0 and has tested the prevention of deepfake attacks using watermark embedded videos comprising 8,074 frames from different benchmark datasets. The proposed approach has acquired a 100% success rate in preventing deepfake attacks. Our code is available at https://github.com/shahidmuneer/deepfakes-watermarking-technique.

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Section: Methodsmentioning

confidence: 99%