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
It is argued PPAR can move from prototypes toward deployment, with lessons extending to face recognition and medical imaging, and a roadmap centered on benchmark standardization is contributed.
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 dependen...
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
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 is proposed.
Basudha Pal, Arjun Narayanan, Neha Ajith et al.· 0 citations
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