Summary of Benet: a Cross-domain Robust Network For Detecting Face Forgeries Via Bias Expansion and Latent-space Attention, by Weihua Liu et al.
BENet: A Cross-domain Robust Network for Detecting Face Forgeries via Bias Expansion and Latent-space Attention
by Weihua Liu, Jianhua Qiu, Said Boumaraf, Chaochao lin, Pan liyuan, Lin Li, Mohammed Bennamoun, Naoufel Werghi
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces BENet, a Cross-Domain Robust Bias Expansion Network designed to detect fake faces more effectively. The authors address limitations in current detectors by introducing a bias expansion module based on autoencoders, which maintains genuine facial features while enhancing differences in fake reconstructions. A Latent-Space Attention (LSA) module is also introduced to capture inconsistencies related to fake faces at different scales. BENet integrates a cross-domain detector module that verifies the facial domain during inference, improving recognition accuracy for unknown sources. The network is trained end-to-end with a novel bias expansion loss, and experiments demonstrate its superiority over current state-of-the-art solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BENet is a new way to detect fake faces on the internet. This problem is important because fake faces can be used to spread misinformation or harm people’s reputations. The BENet team created a special kind of computer network that can recognize fake faces better than other networks. They used a combination of techniques, including something called “autoencoders,” which help the network learn what makes real faces different from fake ones. The network also has a special feature that helps it detect fake faces even when they’re very similar to real ones. The team tested BENet and found that it works better than other methods for detecting fake faces. |
Keywords
» Artificial intelligence » Attention » Inference » Latent space
