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The issue of detecting deepfakes has garnered significant attention in theresearch community, with the goal of identifying facial manipulations for abuseprevention. Although recent studies have focused on developing generalizedmodels that can detect various types of deepfakes, their performance is notalways be reliable and stable, which poses limitations in real-worldapplications. Instead of learning a forgery detector, in this paper, we proposea novel framework - Integrity Encryptor, aiming to protect portraits in aproactive strategy. Our methodology involves covertly encoding messages thatare closely associated with key facial attributes into authentic images priorto their public release. Unlike authentic images, where the hidden messages canbe extracted with precision, manipulating the facial attributes throughdeepfake techniques can disrupt the decoding process. Consequently, themodified facial attributes serve as a mean of detecting manipulated imagesthrough a comparison of the decoded messages. Our encryption approach ischaracterized by its brevity and efficiency, and the resulting method exhibitsa good robustness against typical image processing traces, such as imagedegradation and noise. When compared to baselines that struggle to detectdeepfakes in a black-box setting, our method utilizing conditional encryptionshowcases superior performance when presented with a range of different typesof forgeries. In experiments conducted on our protected data, our approachoutperforms existing state-of-the-art methods by a significant margin.

Building an Invisible Shield for Your Portrait against Deepfakes