Most adversarial attacks on point clouds perturb a large number of points, causing widespread geometric changes and limiting applicability in real-world scenarios. While recent works explore sparse attacks by modifying only a few points, such approaches often struggle to maintain effectiveness due to the limited influence of individual perturbations. In this paper, we propose SCP, a sparse and cooperative perturbation framework that selects and leverages a compact subset of points whose joint perturbations produce amplified adversarial effects. Specifically, SCP identifies the subset where the misclassification loss is locally convex with respect to their joint perturbations, determined by checking the positive-definiteness of the corresponding Hessian block. The selected subset is then optimized to generate high-impact adversarial examples with minimal modifications. Extensive experiments show that SCP achieves 100% attack success rates, surpassing state-of-the-art sparse attacks, and delivers superior imperceptibility to dense attacks with far fewer modifications.

The rising sophistication of digital surveillance poses hurdles for concealing sensitive data within innocuous communication channels.Conventional image steganography relies on detectable pixel-level perturbations.In this paper, we introduce a novel steganography framework that fundamentally reorients the steganographic containers from the visual domain to the linguistic domain.To seamlessly bridge the gap from raw pixels to discriminative logits, we leverage the reversible latent space of discrete diffusion models to compress high-resolution secret images into lightweight binary payloads.The semantic stability of textual data ensures the integrity of the hidden payload across diverse platforms.Extensive evaluations confirm that this cross-modal approach establishes a superior equilibrium between embedding capacity and statistical undetectability in comparison to existing paradigms.

Secure and undetectable transmission of visual intelligence through open networks presents a critical technological challenge in today's heavily monitored digital world. Text-based encryption is insufficient for massive, multimodal data from ubiquitous devices. The fundamental dilemma lies in transmitting large-scale visual data through noisy, adversarial channels while ensuring seamless concealment and robust recovery. In this paper, we bridge the gap by combining diffusion models with the universal presence and robustness of Quick Response (QR) codes for covert communication. Grounding steganographic security in the normative patterns of machine-readable symbols decouples it from the statistical properties of natural images. The reconstructions reside on the same semantic manifold as the originals, ensuring their functional equivalence in downstream tasks and maintaining robustness against quality degradation that would cripple pixel-based methods. Our training-free model compresses complex visual information into binary representations that integrate seamlessly into the data region of standard-compliant QR codes. This approach facilitates covert data transmission while preserving the full functionality and readability of the QR container. We achieve a compression ratio exceeding 400:1 while retaining semantic information for high-quality reconstruction. The preservation of all functional QR code components ensures complete compatibility with standard readers while concealing substantial visual content. Experimental results show a superior balance of security and performance compared to existing methods, all of which require no extra training.

The rapid advancement of big data and artificial intelligence (AI) in healthcare heightens the urgency for accurate medical text sentiment analysis. The privacy protection of medical data has been a crucial concern due to its sensitivity. The Internet of Medical Things (IoMT) facilitates large-scale data collection at lower cost, enabling precision medicine. However, decentralized IoMT poses novel challenges to centralized standard encryption schemes. In this article, we propose a novel approach to building privacy-preserving sentiment models with a generative pretrained transformer (GPT). We first convert sensitive medical text data into noise-like and distributed one-hot images. Then, we introduce visual cryptography (VC) for lightweight and secure transmission of medical text across public networks in resource-limited IoMT devices. We adopt a cross-domain sentiment analysis framework that finetunes transformer-based language models for accurate sentiment analysis instead of training GPT in sentiment analysis from scratch. Experimental results show that the proposed approach improves the accuracy and effectiveness of sentiment analysis while maintaining privacy, thereby addressing a significant gap in biomedical text analysis.

The recent advancement of the Internet of Things (IoT) and information technology has led to the rapid expansion of interconnectivity among a billion devices across various applications. The advent of massive data has resulted in greater computational dependence, posing obstacles to applying security policies in energy-sensitive devices. However, public-key-based encryption algorithms are impractical or impossible to execute on these resource-limited terminals. In this paper, we propose a lightweight framework called STBCIoT based on a visual cryptography (VC) scheme to achieve low-latency encryption for large-scale data like biometric images. To reduce noise in encryption, we utilize central recognition and gray-level features of QR codes to integrate the visually friendly feature of QR into VC. we further propose a high-quality image generation model with the halftoning effect of VC to improve the quality of decrypted images. The experimental results demonstrate that our proposed method achieves high recognition performance on lossy decrypted images, effectively overcoming the performance limitations of traditional public key encryption methods for largescale images.

The integration of healthcare monitoring with Internet of Things (IoT) networks radically transforms the management and monitoring of human well-being. Portable and lightweight electroencephalography (EEG) systems with fewer electrodes have improved convenience and flexibility while retaining adequate accuracy. However, challenges emerge when dealing with real-time EEG data from IoT devices due to the presence of noisy samples, which impedes improvements in brainwave detection accuracy. Moreover, high inter-subject variability and substantial variability in EEG signals present difficulties for conventional data augmentation and subtask learning techniques, leading to poor generalizability. To address these issues, we present a novel framework for enhancing EEG-based recognition through multi-resolution data analysis, capturing features at different scales using wavelet fractals. The original data can be expanded many times after continuous wavelet transform (CWT) and recombination, alleviating insufficient training samples. In the transfer stage of deep learning (DL) models, we adopt a subtask learning approach to train the recognition model to generalize efficiently. This incorporates wavelets at various scales instead of exclusively considering average prediction performance across scales and paradigms. Through extensive experiments, we demonstrate that our proposed DL-based method excels at extracting features from small-scale and noisy EEG data. This significantly improves healthcare monitoring performance by mitigating the impact of noise introduced by the external environment.

Visual cryptography scheme (VCS) is a secret‐sharing scheme which encrypts images as shares and can decrypt shares without digital devices. Although a participant can reveal the secret image by merely stacking a sufficient number of shares, the visual quality of recovered images is reduced, and malicious adversaries can cheat participants by giving faked shares. The paper presents a novel VCS called T‐VCS (trusted VCS) which consists of two main components: a high‐quality VCS and an enhanced verification scheme of shares based on the emerging Intel Software Guard eXtensions (SGX). While providing high‐quality recovery, T‐VCS keeps the size of the shares the same as the original secret image. We use SGX to act as a trusted third party (TTP) to verify the validity of the shares in an attested enclave without degrading the image quality. The experimental results show that T‐VCS can achieve a balance among contrast, share size, and verification efficiency.