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.
By Teruki Sano, Minoru Kuribayashi, Masao Sakai, Shuji Isobe, Eisuke Koizumi, Zhang Zhang, Satoru Matsumoto
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
arXiv:2503.05794v4 Announce Type: replace-cross
Abstract: Speaker verification models are trained on large-scale public datasets whose licenses usually prohibit unauthorized commercial use, yet such...
By Yiming Li, Kaiying Yan, Jiawen Diao, Shuo Shao, Tongqing Zhai, Shu-Tao Xia, Dacheng Tao
arXiv:2410. 01574v4 Announce Type: replace-cross Abstract: The rapid advancement of Generative Artificial Intelligence (GenAI) capabilities is accompanied by a concerning rise in its misuse.
By Sina Mavali, Jonas Ricker, David Pape, Asja Fischer, Lea Sch\"onherr
arXiv:2510. 10982v2 Announce Type: replace-cross Abstract: Recent AI regulations increasingly emphasize the need for mechanisms that preserve the utility of data for AI innovation while preventing misuse, particularly by enforcing purpose limitation in downstream AI applications.
By Zihan Wang, Zhiyong Ma, Zhongkui Ma, Shuofeng Liu, Akide Liu, Derui Wang, Minhui Xue, Guangdong Bai
arXiv:2501. 15509v5 Announce Type: replace-cross Abstract: Model fingerprinting has emerged as a crucial mechanism for safeguarding the intellectual property of open-source models, offering a non-intrusive approach that requires no modifications to the protected model.
By Shuo Shao, Haozhe Zhu, Yiming Li, Hongwei Yao, Tianwei Zhang, Zhan Qin