Yingchao Wang

Shulin Lan, Kanlin Liu, Yazhou Zhao et al.

Current passive deepfake face-swapping detection methods encounter significance bottlenecks in model generalization capabilities. Meanwhile, proactive detection methods often use fixed watermarks which lack a close relationship with the content they protect and are vulnerable to security risks. Dynamic watermarks based on facial features offer a promising solution, as these features provide unique identifiers. Therefore, this paper proposes a Facial Feature-based Proactive deepfake detection method (FaceProtect), which utilizes changes in facial characteristics during deepfake manipulation as a novel detection mechanism. We introduce a GAN-based One-way Dynamic Watermark Generating Mechanism (GODWGM) that uses 128-dimensional facial feature vectors as inputs. This method creates irreversible mappings from facial features to watermarks, enhancing protection against various reverse inference attacks. Additionally, we propose a Watermark-based Verification Strategy (WVS) that combines steganography with GODWGM, allowing simultaneous transmission of the benchmark watermark representing facial features within the image. Experimental results demonstrate that our proposed method maintains exceptional detection performance and exhibits high practicality on images altered by various deepfake techniques.

Yingchao Wang, Wenqi Niu

Personalized Federated Learning (PFL) focuses on tailoring models to individual IIoT clients in federated learning by addressing data heterogeneity and diverse user needs. Although existing studies have proposed effective PFL solutions from various perspectives, they overlook the issue of forgetting both historical personalized knowledge and global generalized knowledge during local training on clients. Therefore, this study proposes a novel PFL method, Federated Progressive Self-Distillation (FedPSD), based on logits calibration and progressive self-distillation. We analyze the impact mechanism of client data distribution characteristics on personalized and global knowledge forgetting. To address the issue of global knowledge forgetting, we propose a logits calibration approach for the local training loss and design a progressive self-distillation strategy to facilitate the gradual inheritance of global knowledge, where the model outputs from the previous epoch serve as virtual teachers to guide the training of subsequent epochs. Moreover, to address personalized knowledge forgetting, we construct calibrated fusion labels by integrating historical personalized model outputs, which are then used as teacher model outputs to guide the initial epoch of local self-distillation, enabling rapid recall of personalized knowledge. Extensive experiments under various data heterogeneity scenarios demonstrate the effectiveness and superiority of the proposed FedPSD method.