arXiv AI
Dual-Locking Learned AI Models: A PIN-Based Sparse QIM Watermarking and Adaptive Index Permutation Approach
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arXiv Machine Learning
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A Robust Watermark-based Fingerprint Framework for GNNs Ownership Verification
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.
arXiv Machine Learning
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CertDW: Towards Certified Dataset Ownership Verification via Conformal Calibration
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.
arXiv Machine Learning
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MeMark: Membrane-Space Watermarking for Spiking Neural Networks
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.
arXiv AI
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Attribute-based Undetectable Watermarking for Generative AI Models
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.
arXiv Computer Vision
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FeatMark: Feature-level Watermark Protection against Mimicry Attacks with Diffusion Models
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.
arXiv Machine Learning
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Keyed Provenance Watermarking with Complementary Lattice-Based Secure Aggregation for Federated Learning
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,...