Robust Deep Hashing Against Adversarial Attacks through Self-Supervised Semantic Consistency Optimization - Warren Jacobs; Ranfei Meng (International Journal of Clinical and Translational Medicine, 2026)
Deep hashing has become a cornerstone of large-scale multimedia retrieval, enabling compact binary representations that support rapid similarity search in high-dimensional spaces. Despite their efficiency, deep hashing systems exhibit pronounced vulnerability to adversarial perturbations, where visually imperceptible modifications can catastrophically alter hash codes and thus the entire retrieval outcome. This paper presents a system-oriented investigation into robust deep hashing through self-supervised semantic consistency optimization. We argue that adversarial robustness in hashing must be addressed not as an isolated algorithmic tweak but as a system-level property encompassing representation design, training methodology, deployment infrastructure, and governance. We introduce a conceptual framework that couples self-supervised learning principles with consistency enforcement across multiple semantically equivalent views of the input, thereby steering the hash function toward invariant and semantically stable regions of the embedding space without relying on explicit adversarial training or labeled data. The paper analyzes structural trade-offs among hash length, code balance, retrieval speed, storage cost, energy footprint, and robustness margins. Beyond technical performance, we examine sociotechnical dimensions such as fairness in retrieval outcomes across demographic subgroups, the sustainability implications of adversarial defense strategies, and the regulatory requirements emerging from high-stakes deployment contexts. Cross-domain comparisons with perceptual hashing, blockchain-anchored integrity schemes, and large-scale cloud indexing architectures enrich the discussion. By positioning robust deep hashing within a broader infrastructure and policy landscape, we provide actionable insights for designing retrieval systems that are not only accurate and fast but also trustworthy, equitable, and resilient under active adversarial environments.