ABSTRACT Deep learning methods for image forgery detection often struggle with compression attack robustness. This paper proposes a novel multi‐class forgery detection framework combining spatial‐frequency fusion with Swin‐Transformer, outperforming existing methods in compression attack scenarios. Our approach integrates a frequency domain perception module with quantization tables, a spatial domain perception module through multi‐strategy convolutions, and a dual‐attention mechanism combining spatial and channel attention for feature fusion. Experimental results demonstrate superior performance with an F 1 score of 87% under JPEG compression ( q = 75), significantly surpassing current state‐of‐the‐art methods by an average of 15% in compression resistance while maintaining high detection accuracy.

In the era of the meta-universe and the proliferation of personalized social networks, interactive behaviors like sharing personal and family photos pose an escalating risk of privacy breaches and identity exposure. A potential remedy lies in substituting real images with anonymized face images in public contexts. While existing face anonymization methods often replace substantial portions of face images, the resultant faces lack sufficient similarity to the originals. To address this, we propose a anonymization model leveraging saliency analysis to detect identity relevant facial region, preserving visual coherence and avoiding recognition by face recognition systems. Our model comprises two integral networks: the Dynamic Identity Perception Network (DIPNet) and the improved PSPNet. DIPNet, in particular, encompasses two vital sub-modules: the dynamic region perception module detects identity relevant region; the anonymization region control module governs the size of region through thresholding, thereby dominating the preservation of identity independent features and the degree of anonymization. The improved PSPNet produces high-quality identity anonymized faces. Experimental results demonstrate that our method yields realistic anonymized faces, retaining original features and deceiving face recognition systems, safeguarding privacy in the modern digital landscape.

Abstract Although graph convolutional networks have achieved good performances in skeleton‐graph‐based action recognition, there are still some problems which include the incomplete utilization of skeleton graph features and the lacking of logical adjacency information between nodes in adjacency matrix. In this article, a human action recognition algorithm is proposed based on multiple features from the skeleton graph to solve these problems. More specifically, an improved adjacency matrix is constructed to make full use of the multiple skeleton graph features. These features include local differential features, multi‐scale edge features, features of the original skeleton graph, nodal features, and nodal motion features. Extensive results are conducted on four standard datasets (NTU RGB‐D 60, NTU RGB‐D 120, Kinetics, and Northwestern‐UCLA). The experimental results show that the proposed algorithm outperforms the SOTA action recognition algorithms.

Unsupervised domain adaptation (UDA) attempts to learn domain invariant representations and has achieved significant progress, whereas self-training-based UDA methods have shown powerful performance. However, due to the domain gap, pseudo-labels selected through high confidence scores or uncertainty inevitably contain noise, leading to inaccurate predictions. To address this issue, we propose a novel risk-consistent training method. Specifically, both clean and noisy classifiers are introduced to estimate the noise transition matrix. The clean classifier is exploited to assign pseudo-labels for target data in each iteration. The noisy classifier is then trained with noisy target samples, and the optimal parameters are obtained through a closed-form solution. Heuristically, we also pre-train a domain predictor to select a target-like source example for the noise transition matrix estimation. In addition, we design an uncertainty-guided regularization to generate soft pseudo-labels and avoid overconfident predictions. Extensive experimental results show the effectiveness of our method, and state-of-the-art performance has been achieved. Codes are available athttps://github.com/feifei-cv/RCE.