Aug 2026· Machine Vision and Applications· Vol 37· 0 citations· 60 references
Computer Science
TL;DR
A unified framework called Friend-Safe Semantic-Aware Reversible Adversarial Examples (FS-SA-RAE), which integrates three complementary mechanisms: a friend-safe attack that employs dual-objective optimization to fool unauthorized (foe) models while preserving classification accuracy for authorized (friend) models, enabling selective access control and reversible data hiding based on histogram shifting.
Experimental results support that the proposed framework has succeeded in providing better adversarial robustness while preserving data privacy, which is acceptable in cloud-IoT environments for secure medical image analysis.
Vijayalakshmi MM, Neelam Malayadri· International journal of com...· 0 citations
Video surveillance in public safety, healthcare, and smart environments has made continuous human monitoring routine, raising real risks to personal identity and appearance. Privacy-preserving action recognition (PPAR) tackles the tension between the utility of video understanding and this exposure, and has drawn fast-growing interest. However, existing surveys remain narrow. Most catalog a single mechanism family, predate recent adversarial and hybrid work, or barely address evaluation. The result is a fragmented literature with incompatible threat models, inconsistent metrics, and no shared evaluation standard. We address this with a PRISMA-guided review of 32 peer-reviewed papers (2018--2026) drawn from 885 screened records. Methods sort into five families, namely adversarial learning (52%), skeleton-based (20%), cryptographic (12%), differential privacy (8%), and hybrid (8%), each with distinct privacy, utility, and efficiency trade-offs. Evaluation is the weak point. Only 10% of papers adopt a formal privacy definition, 65% rely on ad-hoc metrics, and 40% report an inconsistently defined cMAP. The trade-offs are steep. Skeleton methods reach about 85% accuracy but drop appearance, adversarial methods hold near 80% utility at moderate privacy (cMAP 0.9 to 0.3--0.5), and differential privacy often falls below 70%. Harder conditions stay under-tested, with fewer than 15% of papers checking cross-dataset generalization, under 10% testing adaptive attackers, and real-time edge deployment nearly untouched. We contribute a two-dimensional privacy-space taxonomy, a formal threat model, a comparative trade-off analysis, the PPAR Unified Evaluation Protocol, and a roadmap centered on benchmark standardization. With this grounding, we argue PPAR can move from prototypes toward deployment, with lessons extending to face recognition and medical imaging.
Sareer Ul Amin, Muhammad Ayaz, M. Munsif et al.· 0 citations
Deep Neural Networks (DNNs) remain vulnerable to adversarial perturbations, raising significant concerns in image processing applications, particularly in high-stakes domains such as medical imaging and security-critical systems. Most existing defense strategies are limited by domain specificity, architectural dependence, or the need for extensive retraining, making them impractical for real-world deployment. In this work, we propose ADEx, the first framework to integrate low-rank image approximation with explainability-driven analysis for the detection of adversarial samples. ADEx works by extracting a low-rank representation of the input image using Singular Value Thresholding (SVT), and identifying important image regions by computing class-specific gradient maps from the final layers of the classifier. These maps are then compared using Rank-Biased Overlap (RBO) to quantify the degree of attention drift induced by adversarial perturbations. ADEx is designed for adversarial detection in image classification systems, where class-specific gradient-based explanations are well defined. The framework operates without retraining or architectural modification and can be applied to a wide range of differentiable classifiers, provided gradient access is available for explanation generation. Extensive experiments across multiple datasets, architectures, and attack types demonstrate consistent performance, robustness to hyperparameter choices, and low sensitivity to calibration size. The method provides an interpretable and lightweight solution suitable for practical deployment.
Syamantak Sarkar, Nirmal Joseph, Sudhish N. George et al.· IEEE Transactions on Image P...· 0 citations
This paper presents a systematic framework for membership inference attacks, in which an adversary with only black-box query access to a deployed classifier determines whether a specific individual's record was part of its training set.
Pramod Prakash· International Journal of Int...· 0 citations
ClinX is introduced, an end-to-end multimodal PHI sanitization framework for medical image-text data, and results show that OCR-only masking is not sufficient as a standalone solution, and restoration-based sanitization better preserves clinically relevant visual context while sharply reducing recoverable PHI.
S. Shrestha, Zongxing Xie, Chen Zhao et al.· 0 citations
As artificial intelligence is increasingly integrated into chest X-ray (CXR) interpretation, triage, and clinical decision support, understanding its vulnerability to adversarial manipulation is critical for safe deployment. Existing robustness evaluations, however, predominantly rely on pixel-space attacks that introduce numerically constrained perturbations but may not represent plausible radiographic variation. This limitation is particularly important in multi-disease CXR classification, where models simultaneously evaluate multiple overlapping pathologies and adversarial failures may alter several diagnostic predictions. We propose a text-guided diffusion-based adversarial framework that optimizes learnable text conditioning while keeping the diffusion generator and target classifier frozen, enabling adversarial generation through a learned image prior rather than direct pixel manipulation. We evaluate the framework across multiple classifier architectures in both binary atelectasis and multi-disease CXR classification and compare it with FGSM, PGD, and Carlini-Wagner attacks. Our approach consistently produced the greatest degradation in classifier performance, reducing AUROC to 0.3885-0.5646 in binary classification and 0.4441-0.4878 in the multi-disease setting, while achieving superior image fidelity (SSIM 0.9080, LPIPS 0.1670, FID 51.23). Importantly, clinician interpretation remained unchanged for 95.9% of binary and 73.8% of multi-disease adversarial images despite substantial changes in model predictions. These findings reveal a clinically important discrepancy between human and machine interpretation and demonstrate the need to extend medical AI robustness evaluation beyond conventional pixel-space attacks toward generative threat models that can expose failures under visually and clinically plausible image variations.
Basudha Pal, Arjun Narayanan, Neha Ajith et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
