Building an Invisible Shield for Your Portrait against Deepfakes
Jiazhi Guan Tianshu Hu Hang Zhou Zhizhi Guo Lirui Deng Chengbin Quan Errui Ding Youjian Zhao
The issue of detecting deepfakes has garnered significant attention in the
research community, with the goal of identifying facial manipulations for abuse
prevention. Although recent studies have focused on developing generalized
models that can detect various types of deepfakes, their performance is not
always be reliable and stable, which poses limitations in real-world
applications. Instead of learning a forgery detector, in this paper, we propose
a novel framework - Integrity Encryptor, aiming to protect portraits in a
proactive strategy. Our methodology involves covertly encoding messages that
are closely associated with key facial attributes into authentic images prior
to their public release. Unlike authentic images, where the hidden messages can
be extracted with precision, manipulating the facial attributes through
deepfake techniques can disrupt the decoding process. Consequently, the
modified facial attributes serve as a mean of detecting manipulated images
through a comparison of the decoded messages. Our encryption approach is
characterized by its brevity and efficiency, and the resulting method exhibits
a good robustness against typical image processing traces, such as image
degradation and noise. When compared to baselines that struggle to detect
deepfakes in a black-box setting, our method utilizing conditional encryption
showcases superior performance when presented with a range of different types
of forgeries. In experiments conducted on our protected data, our approach
outperforms existing state-of-the-art methods by a significant margin.