Adding SE attention blocks to a small CNN raises deepfake detection accuracy from 91.13% to 94.14% on the StyleGAN subset of DFFD, but the result rests on a single run with questionable comparisons.
Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM
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abstract
Social media is currently being used by many individuals online as a major source of information. However, not all information shared online is true, even photos and videos can be doctored. Deepfakes have recently risen with the rise of technological advancement and have allowed nefarious online users to replace one face with a computer generated face of anyone they would like, including important political and cultural figures. Deepfakes are now a tool to be able to spread mass misinformation. There is now an immense need to create models that are able to detect deepfakes and keep them from being spread as seemingly real images or videos. In this paper, we propose a new deepfake detection schema using two popular machine learning algorithms.
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Enhancing Deepfake Detection using SE Block Attention with CNN
Adding SE attention blocks to a small CNN raises deepfake detection accuracy from 91.13% to 94.14% on the StyleGAN subset of DFFD, but the result rests on a single run with questionable comparisons.