The widespread application of face recognition technology easily leads to the leakage of facial soft biometric privacy. Therefore, image-level face privacy-enhancing methods for face recognition scenarios aim to remove soft biometric privacy information while preserving individual identity. Existing methods suffer from performance degradation in face recognition and poor quality in privacy-enhanced images when processing high-resolution images, often resulting in artifacts and complexity in model optimization. To address these issues, we propose an identity-preserving face privacy-enhancing method based on latent optimization and face masking for high-resolution images. The proposed method generates privacy-enhanced faces by optimizing latent codes and mask blending, without relying on a well-defined encoder-decoder structure, making it more suitable for processing high-resolution images. We introduce a face masking strategy and design a facial elements identity influence prioritization module to adaptively select privacy-enhancing facial parts and attributes. As a result, the proposed method can protect multiple facial privacy attributes while improving the quality of generated images and maintaining face recognizability. Extensive experiments on two benchmark datasets demonstrate the effectiveness of the proposed method in anonymizing visual appearances for high-resolution face images while maintaining superior face recognition performance and image quality.

Recently, deepfakes have raised severe concerns about the authenticity of online media. Prior works for deepfake detection have made many efforts to capture the intra-modal artifacts. However, deepfake videos in real-world scenarios often consist of a combination of audio and visual. In this paper, we propose an Audio-Visual Joint Learning for Detecting Deepfake (AVoiD-DF), which exploits audio-visual inconsistency for multi-modal forgery detection. Specifically, AVoiD-DF begins by embedding temporal-spatial information in Temporal-Spatial Encoder. A Multi-Modal Joint-Decoder is then designed to fuse multi-modal features and jointly learn inherent relationships. Afterward, a Cross-Modal Classifier is devised to detect manipulation with inter-modal and intra-modal disharmony. Since existing datasets for deepfake detection mainly focus on one modality and only cover a few forgery methods, we build a novel benchmark DefakeAVMiT for multi-modal deepfake detection. DefakeAVMiT contains sufficient visuals with corresponding audios, where any one of the modalities may be maliciously modified by multiple deepfake methods. The experimental results on DefakeAVMiT, FakeAVCeleb, and DFDC demonstrate that the AVoiD-DF outperforms many state-of-the-arts in deepfake detection. Our proposed method also yields superior generalization on various forgery techniques.