Deepnude Forensics: Detection, Attribution, and Defense Against AI-Generated Intimate Image Abuse

The commoditization of open-weight generative image models has collapsed the technical and eco- nomic barriers to producing non-consensual intimate imagery (“deepnudes”) and, in its most severe form, synthetic Child Sexual Abuse Material (CSAM). Unlike photographic abuse material, synthetic content is unbounded in volume, detaches from any specific victim-identification signal, and sys- tematically defeats the perceptual-hash infrastructure (PhotoDNA, Project Arachnid, INTERPOL ICSE) on which global child-protection operations depend. Building on our prior work on machine- learning deep nude detection, this paper first develops the theoretical foundations of deep nude detection techniques—decomposing the task into explicitness, manipulation, and subject-attribute factors with a decision- and information-theoretic account of multi-cue fusion—and then presents an end-to-end, defense-oriented framework with cybersecurity at its core that (i) formally charac- terizes the open “uncensored” generation ecosystem and the model-level mechanisms by which safety alignment is stripped, treated strictly as a threat model rather than a recipe; (ii) formalizes the reverse engineering of generative pipelines for attribution—tracing a synthetic artifact back to its originating model family through diffusion reconstruction error, latent and weight-space fingerprints, spectral forensics, membership inference, and foundation-model feature forensics; (iii) specifies a multi-stream detection architecture, with algorithms, designed to be trained without possessing illegal material, using a curated-benign public track with suspect material confined to accredited enclaves, benign adult/synthetic proxies, and a privacy-preserving federated protocol; (iv) analyzes preemptive defenses—adversarial immunization (PhotoGuard, Glaze, Nightshade), data poisoning, and prove- nance watermarking (C2PA, SynthID, Stable Signature)—together with their documented robustness failures under purification and adaptive attack; and (v) maps the whole to operational law-enforcement workflows (INTERPOL, NCMEC, Canadian Centre for Child Protection), to a multi-jurisdiction legal analysis, and to forensic-admissibility and chain-of-custody constraints. We contribute a layered risk taxonomy, a reproducible evaluation protocol and benchmark design with explicit ethical controls, a cybersecurity-hardening checklist for the model supply chain, an illustrative (benign) end-to-end walkthrough, and a prioritized research and policy agenda. Our central thesis is that durable defense requires shifting from post hoc content matching toward generative-pipeline forensics: detecting, attributing, and raising the cost of synthesis at the model and provenance layers. We position this work as a critical synthesis, a proposed reference architecture, and an evaluation protocol rather than an empirically validated system: the figures are illustrative of the protocol, not measured results, and we mark throughout what is established in prior work, what is proposed here, and what remains to be validated—particularly for the hashing, attribution, and privacy guarantees, whose scope we state conservatively.