arXiv:2610.01385v1 Announce Type: cross Abstract: This work aims to answer the question of whether it is possible to generate irreversible protected templates when the PolyProtect biometric template...
arXiv:2605.03857v2 Announce Type: replace Abstract: This work presents a deeper analysis of the "irreversibility" property of PolyProtect, a biometric template protection method initially proposed fo...
arXiv:2602.02914v4 Announce Type: replace Abstract: Privacy-preserving face recognition (PPFR) and face anonymization have different goals, but both must retain some identity-related information for...
arXiv:2607. 17504v1 Announce Type: cross Abstract: Split face recognition reduces client-side computation but exposes intermediate features to feature inversion attacks and unauthorized analysis by honest-but-curious (HBC) servers.
Split face recognition reduces client-side computation but exposes intermediate features to feature inversion attacks and unauthorized analysis by honest-but-curious (HBC) servers. Existing privacy-preserving face recognition methods mainly aim to resist unauthorized reconstruction, typically producing features whose inversion yields visibly degraded results, which may reveal the existence of protection and motivate adaptive attacks.
The paper proposes a defense against watermark forgery in generative models by randomizing the selection of watermark keys for each query and accepting content only if exactly one key detects a watermark. This approach provides a sample‑count‑independent upper bound on forgery success for blind attackers while preserving model utility. Experiments show a dramatic drop in forgery success rates—from 87% to 1% with four keys for text and from 100% to 2% for images—without significant computational overhead.
arXiv:2606. 18120v1 Announce Type: cross Abstract: Large language model applications build prompts from templates, and Handlebars is a widely used templating engine and the default prompt-template format in Microsoft Semantic Kernel.
The paper presents KVeil, the first‑place solution to the ECCV 2026 LifeGenIP Challenge on Unlearnable Videos. It introduces Reference Capacity Collapse and a Motion‑Guided EOT mechanism for Phase 1, and a two‑layer framework that protects facial and background regions for Phase 2, achieving strong protection against various customization attacks. The methods build on the authors’ prior work LETHE and are publicly available on GitHub.
arXiv:2606. 27976v1 Announce Type: cross Abstract: Dense embeddings underpin semantic search and RAG, yet a leaked vector store hands much of the underlying text back to whoever holds it.
The paper compares four audit methods for assessing identity‑level differential privacy in pre‑trained, black‑box face generators. Each method—GaussMech, KDE‑LR, MMD‑TV, and ROC‑HT—has distinct assumptions, hyperparameters, and finite‑sample limitations, and they produce markedly different epsilon estimates when applied to FaceFusion and InstantID. The study finds that all methods reveal significant identity distinguishability, but none can be reliably ranked in this high‑distinguishability regime, suggesting that future work should evaluate them on partially private mechanisms.
The paper introduces CARTS, a steganographic method that uses autoregressive language models to encode a payload text into a stegotext of identical token length by preserving per‑position rank information across contexts. It provides a formal security analysis, proving exact correctness under deterministic model assumptions, and defines key security notions such as context search, key collisions, message equivocation, and non‑commutativity of encoding maps. Empirical tests on Llama 3 8B confirm perfect payload recovery, no random key collisions, and no commuting key pairs, indicating resistance to the studied attack vectors.
arXiv:2608.30087v1 Announce Type: new Abstract: Unlearning an identity from a face-conditioned generator by redirecting its conditioning embedding can silently fail if the redirected output is still...