Rushi Lan

Recently, thumbnail-preserving encryption (TPE) has attracted significant attention because it maintains certain visual information in encrypted images, thereby achieving a trade-off between visual usability and privacy protection. However, existing TPE schemes utilize independent encryption between pixels, blocks, or channels, which results in excessive robustness and consequently introduces security vulnerabilities. To address this issue, we propose a triple-chain architecture to achieve sensitive TPE. This architecture integrates three interdependent encryption chains at the pixel, block, and channel levels, establishing cryptographic dependencies such that any minor modification to either the original or encrypted image leads to completely divergent encryption/decryption outcomes. Specifically, the architecture introduces three chained encryption mechanisms: (1) pixel-level chained encryption propagates encrypted pixels outputs as the part of subsequent inputs; (2) block-level hashing derives new keys from plaintext-key concatenations; and (3) channel-level dependencies are established via iterative key derivation. This cascading structure ensures cryptographic interdependency across all levels. The experimental results verify that the proposed architecture achieves sensitive TPE for the first time while preserving the TPE characteristics of visual usability and privacy protection.

Speech signals, owing to their non-invasive and low-cost advantages, have emerged as a pivotal modality for the objective assessment of depression. However, existing methods struggle to capture the hierarchical dynamic structures of speech, thereby constraining the discriminability of the representations. To address this issue, this paper proposes a Hierarchical Dynamics Aggregation Network (HDAN). Under the hierarchical dynamic modeling paradigm, the model first constructs context-aware first-order acoustic state representations. On this basis, a Multi-Scale Dynamics Extraction (MDE) module and a Dynamic Relation Network (DRN) are introduced to extract and aggregate speech dynamics across multiple temporal scales, forming a unified global dynamic representation. Then, a Dynamic Synergistic Memory (DSM) module is employed to align and enhance sample-level dynamics with learnable prototypes. Finally, a Mask-based Cross Fusion (MCF) module is used to adaptively fuse global dynamics and content semantics, obtaining a joint representation that accounts for both content and dynamics. Comparative experiments on the Androids Corpus and Clinical Dataset demonstrate that HDAN consistently outperforms multiple baseline models on various metrics, validating the effectiveness of HDAN. Meanwhile, ablation studies show that each submodule contributes positively to performance improvements, further supporting the rationality of its structural design.

Using image-level weakly supervised semantic segmentation (WSSS) techniques to segment tissue regions in giga-pixel histopathological whole slide images (WSI) has garnered widespread attention, as it can reduce many annotation workloads for pathologists. Most recent studies are based on class activation mapping (CAM) to generate pseudo masks, which are then used to train segmentation model in a fully supervised manner. However, it is still a challenge to accurately segment non-predominant tissue categories due to the existence of long-tailed and inter-class homogeneity matters. For these matters, we propose three designs to solve them: 1) Diffusion-based Data Generation to synthesis new images of tail class to expand data distribution; 2) Feature Recalibration to reassign the logits in CAM to narrow the feature-level prediction gap between predominant and non-predominant classes; 3) Grade-skip Learning to correct the under-fitting tendency of hard samples during the segmentation phase. Moreover, we also design a powerful pipeline LoHo for histopathology tissue segmentation. Extensive experiments demonstrate that our method not only achieves new state-of-the-art performances but also significantly improves segmentation of tail classes. In addition, our methods are plug-and-play, making it easily integrable into many mainstream WSSS frameworks.

Secure transmission and integrity protection of 3D mesh signals are essential in multimedia, virtual reality, and cloud-based rendering applications. Existing 3D mesh encryption methods mainly focus on geometric confidentiality, while largely neglecting the critical role of mesh topology in authentication and tamper detection. In this letter, a topology-aware authenticated encryption transformation for 3D meshes is proposed. Vertex coordinates are encrypted using AES-CFB to ensure geometric confidentiality, while 3D mesh connectivity information is incorporated into a GMAC-based authentication process to enable topology-aware integrity verification. Any modification to geometry or topology can be reliably detected during the decryption process. Experiments demonstrate near-lossless reconstruction with errors on the order of $10^{-8}$ and a 100% detection rate against ciphertext and topology tampering.

Semi-supervised segmentation (S³) is one of the preferred choices for histopathological image segmentation tasks, while how to improve model’s learning capability for unlabeled data remains a key challenge in S³. The remarkable feature extraction abilities of Segment Anything Model (SAM) offers a potential opportunity. However, SAM’s performance on contextual complex histopathological images is not so desirable due to its limitations in finely capture structural relationships. To address this issue, we propose a novel SAM-based S³ framework QuPaS, which consists of Quantum Force Field (QFF) Finetuning and Adversarial Estimation (AE). QFF covers the shortage of SAM’s limited understanding of spatial structure by simulating intermolecular forces to explore the structural topological relationships between pixel-level features. AE introduces an adversarial estimation network to align the consistency of confidence distributions between different outputs, thereby reducing the interference of incompatible semantic features on the model. Extensive experiments across three challenging histopathological segmentation scenarios have demonstrate that our QuPaS completely outperforms the state-of-the-art S³ methods. Furthermore, QuPaS is able to maintain stable generalization performance on previously unseen domains. The code will be released at: https://github.com/director87/QuPaS.