Can Tainted Pixels Expose Deepfake Videos?

• URL: http://arxiv.org/abs/2608.27492v1
• Date: Wed, 26 Aug 2026 15:39:43 GMT
• Title: Can Tainted Pixels Expose Deepfake Videos?
• Abstract summary: We introduce TaintedPixels, a proactive video-protection method built around an asymmetric visibility trade-off.<n>TaintedPixels injects structured periodic perturbations into the blue channel of facial regions and refines them under-visibility, color-cast, and video-level LPIPS budgets.<n>We believe TaintedPixels is the first proactive defense designed specifically against black-box manipulation tools.
• Score: 8.375316880029109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Publicly-acceesible face-manipulation tools have made deepfake creation accessible to non-expert users. Against these, existing defenses are mostly post-hoc, detecting only after forgery has occurred, and operating on still images rather than videos. Research is lacking in i) the proactive protection of published facial videos against black-box manipulation tools, and in (ii) understanding its perceptual effect on human viewers. We introduce TaintedPixels, a proactive video-protection method built around an asymmetric visibility trade-off: the embedded watermark should remain inconspicuous in the published video but become obvious once a downstream tool manipulates the video. TaintedPixels injects structured periodic perturbations into the blue channel of facial regions and refines them under stripe-visibility, color-cast, and video-level LPIPS budgets, with lightweight motion-adaptive deployment. We believe TaintedPixels is the first proactive defense designed specifically against black-box manipulation tools rather than image-level pipelines or specific surrogate generators. Across three publicly available off-the-shelf video manipulation tools and two off-the-shelf detectors, TaintedPixels attains the highest forgery fake rate while keeping perturbations small (LPIPS = 0.0042). Our non-expert human study, conducted on a diverse set of 300 video stimuli spanning different lighting conditions, backgrounds, and skin tones, shows that protected source videos draw a 3.26% suspicion rate, while forgeries from protected sources are identified as fake much more often than forgeries from unprotected sources (90.72% vs. 56.71%). This validates the effectiveness of TaintedPixels.

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