Learning-based image compression techniques combined with current Transformer models and with checker-board context models have shown the excellent Rate-Distortion performance. However, the mixed structure still has room for optimization in terms of redundancy information and decoding efficiency, while the checkerboard context model has redundancy in capturing correlations between latent represen-tations. To solve these problems, we propose an innovative framework that combines a mixed Transformer-CNN structure with a checkerboard context model. Specifically, we introduce a “Checkerboard Channel-wise Entropy Module” to improve coding efficiency of utilizing contexts through a two-channel decoding method with checkerboard contexts. Then, we propose the “In-slice Odd-even Context”, which improves the handling of spatial redundancy information by adding additional spatial contexts by introducing a checkerboard context model to the original mixed structure with channel contexts and global con-texts. Extensive experimental results demonstrate that our pro-posed method outperforms JPEG, BPG and previous learned image compression on the Kodak dataset.

Website Fingerprinting (WF) attacks significantly threaten user privacy in anonymity networks such as Tor. While numerous defenses have been proposed, they are unable to efficiently defend against recent deep learning based WF attacks. In this paper, we propose Palette, a novel and practical WF defense that utilizes traffic cluster anonymization to protect live Tor traffic. By clustering websites with high similarity in traffic patterns and regulating them into a well-designed uniform pattern for a cluster (i.e., a group of similar websites), Palette prevents attackers from distinguishing between these similar websites within the cluster and further provides a strong anonymity guarantee. Comprehensive evaluations with public real-world datasets show that Palette is superior to the existing defenses, greatly reducing the accuracy of the state-of-the-art (SOTA) WF attacks with acceptable overheads. Furthermore, we implement Palette as a Pluggable Transport in the Tor network. The experiment results demonstrate that, on average, Palette effectively reduces the accuracy of the SOTA WF attacks by 73.60%, which improves the existing defenses by 33.50%-43.47%.