The paper introduces REMARK, a watermark‑based fingerprint framework designed to verify ownership of Graph Neural Networks (GNNs). REMARK generates in‑distribution watermark graphs that maximize output differences between GNN models, thereby reducing performance loss from out‑of‑distribution watermarks. It then extracts robust fingerprints from these output differences, eliminating the need for surrogate models trained on watermark data or reliance on specific output levels, and achieves state‑of‑the‑art verification accuracy across real‑world datasets and GNN architectures.
By Han Zhang, Yan Wang, Guanfeng Liu, Pengfei Ding, Huaxiong Wang, Kwok-Yan Lam
The paper introduces CertDW, a certified dataset watermark and ownership verification method that remains reliable even under malicious perturbations. By leveraging conformal prediction, it defines two statistical measures—principal probability (PP) and watermark robustness (WR)—to evaluate model stability on benign versus watermarked samples. The authors derive certification conditions linking WR to a PP-based threshold and provide a high‑probability bound on false positives, enabling robust ownership verification when a suspicious model’s WR exceeds the PP values of benign models.
By Ting Qiao, Yiming Li, Jianbin Li, Yingjia Wang, Leyi Qi, Junfeng Guo, Ruili Feng, Dacheng Tao
MeMark introduces a watermarking scheme for Spiking Neural Networks that embeds a multi‑bit identifier directly into the membrane state of selected Leaky Integrate‑and‑Fire neurons, rather than in the output head. The watermark is recoverable by comparing neuron firing thresholds, eliminating the need for a learned decoder. Experiments on various SNN architectures—including a 215.4M‑parameter SpikeGPT checkpoint—show that all 20 independent 64‑bit keys reliably pass verification under a 51/64 rule, remain robust after fine‑tuning, pruning, quantization, and output‑head replacement, and are not recovered by random keys or adaptive attacks within the tested threat model.
By Roberto Ria\~no, Gorka Abad, Stjepan Picek, Aitor Urbieta
arXiv:2608. 03174v1 Announce Type: cross Abstract: Generative AI systems increasingly produce content whose provenance is difficult to verify, motivating watermarking techniques for identifying model-generated outputs.
By Miryam Mi-Ying Huang, Chung-Wei Lee, Max Raffel, Er-Cheng Tang
FeatMark is a watermarking framework that protects images from text‑to‑image diffusion model mimicry attacks by embedding small, scene‑consistent micro‑features instead of pixel‑level perturbations. It constructs domain‑specific feature banks, selects executable features, and injects them via mask‑guided concept editing to create highly localized, natural edits. Experiments on VGGFace2, CelebA‑HQ, and WikiArt show FeatMark remains robust against ten strong watermark removal attacks and several adaptive attacks, with minimal impact on perceptual quality and extending to video mimicry scenarios.
By Haoyang Li, Ruoxi Sun, Qingqing Ye, Benjamin Zi Hao Zhao, Yaxin Xiao, Jason Xue, Haibo Hu
arXiv:2608.20580v1 Announce Type: cross
Abstract: Federated learning (FL) is vulnerable to multi-level attacks. However, existing methods address them separately, leaving FL exposed to data leakage,...
By Xinyun Liu, Zhi Lu, Yu Chen, Ronghua Xu
arXiv:2505. 03646v5 Announce Type: replace-cross Abstract: Adversarial robustness of deep autoencoders (AEs) has received less attention than that of discriminative models, although their compressed latent representations induce ill-conditioned mappings that can amplify small input perturbations and destabilize reconstructions.
By Chethan Krishnamurthy Ramanaik, Arjun Roy, Tobias Callies, Eirini Ntoutsi
The paper investigates how reducing numerical precision through model quantization impacts the vulnerability of neural networks to model inversion attacks. It provides theoretical bounds on mutual information changes and identifies data-dependent effects, especially at 4‑bit precision. Based on these findings, the authors propose a privacy‑aware post‑training quantization strategy that allocates bits adaptively, calibrates activation ranges, and jointly optimizes weight and activation scaling to improve inversion resistance while preserving model utility.
By Rongke Liu, Youwen Zhu
The paper investigates latent‑space watermarking using pretrained generators, where a watermark encoder selects latent inputs based on a message and secret key to produce outputs with a specified conditional distribution. For finite alphabets, it derives inner and outer bounds on the rate–key trade‑off and characterizes the capacity region when the generator’s output uniquely determines the latent distribution. The study extends to jointly Gaussian models, identifies key sufficient statistics, optimally allocates secret‑key resources across modes, and analyzes robustness against regeneration attacks, providing compound capacity results and decay rates for repeated attacks.
By Jinwan Jeon, Minju Lee, Sung Hoon Lim
The paper introduces BIND, a framework that binds a human’s biometric data to an AI agent’s identity and task-specific authority, enabling secure, real‑time delegation of control. By generating a token that an AI agent presents to an Identity Auditor, the system performs biometric authentication and recovers the agent’s ID and scope, providing non‑repudiable proof of human oversight. A practical implementation using face features and a fuzzy commitment scheme with turbo error‑correcting codes achieves a 96% true match rate at zero false match rate and supports 1024‑bit agent tokens.
By Joseph Geo Benjamin, Anil K Jain, Karthik Nandakumar
arXiv:2609.40031v1 Announce Type: cross
Abstract: Digital image watermarking is increasingly critical in media contexts, as emerging regulations and industry practices require marking AI-generated co...
By Khaled Abud, Aleksey Yakushev, Aleksandr Akimenkov, Irina Serzhenko, Kirill Aistov, Egor Kovalev, Dmitry Obydenkov, Sergey Lavrushkin, Anastasia Antsiferova, Dmitriy Vatolin, Yury Markin, Kirill Lukianov
Text-to-image diffusion models enable data-efficient "mimicry" attacks, wherein adversaries fine-tune the model on a handful of public photos to synthesize convincing forgeries of a target individual....