White Paper: Examining deepfake detector performance under social media re-encoding
Source Reddit Cybersecurity
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This entry references a white paper analyzing the performance of deepfake detectors when videos/images are re-encoded by social media platforms. The research benchmarks popular open source detectors against synthetic face datasets generated by SDXL and InstantID models. The study aims to evaluate the robustness of detection tools under conditions that mimic real-world social media content transformations. No direct vulnerability or exploit is described.
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Given the continuous improvement of all these AI image/video generation model, I've spent the last three months researching, building datasets, and benchmarking deepfake detector performance as a last frontier. This all cumulated in a white paper that examined the robustness of some popular open source detectors on social media platforms (SDXL + InstantID for generation). It's an interesting read, so I thought I'd share.
Here are the huggingface datasets if you'd like to red team your own detector (let me know how it performs)
Original SDXL+InstantID Benchmark: https://huggingface.co/datasets/danb21/synthetic-face-sdxl-instantid-bench
Follow Up Robustness Study: https://huggingface.co/datasets/danb21/social-media-robustness-sdxl-instantid