arXiv AI
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
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arXiv Machine Learning
Sep 11
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 AI
Aug 11
The Watermark Shortcut: How Provenance Marking Sabotages Audio Deepfake Detection
arXiv:2606. 23335v2 Announce Type: replace-cross Abstract: Provenance watermarking is increasingly treated as a safeguard for synthetic speech, whether built directly into speech-generation models such as Chatterbox, provided through dedicated techniques such as AudioSeal, or deployed by commercial platforms such as ElevenLabs.
Hugging Face Trending Papers
Jun 22
The Watermark Shortcut: How Provenance Marking Sabotages Audio Deepfake Detection
Provenance watermarking is increasingly treated as a safeguard for synthetic speech, whether built directly into speech-generation models such as Chatterbox, provided through dedicated techniques such as AudioSeal, or deployed by commercial platforms such as ElevenLabs. We identify a previously uncharacterized liability: when synthetic speech is watermarked and human speech is not, detectors trained alongside latch onto the watermark as a spurious "watermark => fake" shortcut.
arXiv AI
Sep 25
TP-CRIV: A Framework for Third-Party Challenge-Response Identity Verification of AI Models
The paper introduces TP-CRIV, a framework for verifying the identity of AI models through third‑party challenge‑response interactions without requiring white‑box or API access. TP-CRIV operates in a black‑box setting, using fresh, undisclosed challenges and network isolation to ensure that verification relies solely on the claimant’s local model. The authors demonstrate the approach on ten ImageNet‑pretrained CNNs, achieving clear separation between same and cross‑model responses with statistically calibrated thresholds.
arXiv Computation and Language
Oct 19
Watermarkability: Measuring and Improving Language-Model Compatibility with Logit-Based Watermarks
The paper introduces "watermarkability," a metric that quantifies how compatible a language model is with a logit‑based watermark that boosts the probability of a specified token set during decoding. By measuring the expected minimum KL divergence needed to achieve a target probability mass, the authors can assess and compare models and watermark rules. They propose a two‑stage approach—first training a low‑rank adapter conditioned on the target set, then applying a minimal uniform logit bias—to reduce this cost, achieving significant perplexity reductions across multiple watermark schedules on OPT‑125M and Qwen3‑8B.
Hugging Face Trending Papers
Sep 24
TP-CRIV: A Framework for Third-Party Challenge-Response Identity Verification of AI Models
The paper introduces TP-CRIV, a framework for verifying the identity of AI models through third‑party challenge‑response interactions. It operates without white‑box or API access, relying only on black‑box inference and fresh, undisclosed challenges to gather empirical evidence of model possession. The authors demonstrate the method on ten ImageNet‑pretrained CNNs, achieving clear separation between matching and non‑matching models with statistically calibrated thresholds.