Face-swapping techniques have advanced rapidly with the evolution of deep learning, leading to widespread use and growing concerns about potential misuse, especially in cases of fraud. While many efforts have focused on detecting swapped face images or videos, these methods are insufficient for tracing the malicious users behind fraudulent activities. Intrusive watermark-based approaches also fail to trace unmarked identities, limiting their practical utility. To address these challenges, we introduce FaceTracer, the first non-intrusive framework specifically designed to trace the identity of the source person from swapped face images or videos. Specifically, FaceTracer leverages a disentanglement module that effectively suppresses identity information related to the target person while isolating the identity features of the source person. This allows us to extract robust identity information that can directly link the swapped face back to the original individual, aiding in uncovering the actors behind fraudulent activities. Extensive experiments demonstrate FaceTracer's effectiveness across various face-swapping techniques, successfully identifying the source person in swapped content and enabling the tracing of malicious actors involved in fraudulent activities. Additionally, FaceTracer shows strong transferability to unseen face-swapping methods including commercial applications and robustness against transmission distortions and adaptive attacks.

With the flourishing of the Internet, sharing one's photos or automated processing of faces using computer vision technology has become an everyday occurrence. While enjoying the convenience, the concern for identity privacy is also emerging. Therefore, some efforts introduced the concept of ``password'' from traditional cryptography such as RSA into the face anonymization and deanonymization task to protect the facial identity without compromising the usability of the face image. However, these methods either suffer from the poor visual quality of the synthesis results or do not possess the full cryptographic properties, resulting in compromised security. In this paper, we present the first facial identity cryptography framework with full properties analogous to RSA. Our framework leverages the powerful generative capabilities of StyleGAN to achieve megapixel-level facial identity anonymization and deanonymization. Thanks to the great semantic decoupling of StyleGAN's latent space, the identity encryption and decryption process are performed in latent space by a well-designed password mapper in the manner of editing latent code. Meanwhile, the password-related information is imperceptibly hidden in the edited latent code owing to the redundant nature of the latent space. To make our cryptographic framework possesses all the properties analogous to RSA, we propose three types of loss functions: single anonymization loss, sequential anonymization loss, and associated anonymization loss. Extensive experiments and ablation analyses demonstrate the superiority of our method in terms of the quality of synthesis results, identity-irrelevant attributes preservation, deanonymization accuracy, and completeness of properties analogous to RSA.