透かし評価

Multi-Designated Detector Watermarking for Language Models

Authors: Zhengan Huang, Gongxian Zeng, Xin Mu, Yu Wang, Yue Yu | Published: 2024-09-26 | Updated: 2024-10-01
LLMセキュリティ
ウォーターマーキング
透かし評価

Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble

Authors: Chang Dong, Zhengyang Li, Liangwei Zheng, Weitong Chen, Wei Emma Zhang | Published: 2024-09-04 | Updated: 2024-09-19
敵対的サンプル
評価手法
透かし評価

Unveiling Hidden Visual Information: A Reconstruction Attack Against Adversarial Visual Information Hiding

Authors: Jonggyu Jang, Hyeonsu Lyu, Seongjin Hwang, Hyun Jong Yang | Published: 2024-08-08
ウォーターマーキング
透かし評価
顔認識システム

Treatment of Statistical Estimation Problems in Randomized Smoothing for Adversarial Robustness

Authors: Vaclav Voracek | Published: 2024-06-25 | Updated: 2025-01-20
信頼評価モジュール
評価手法
透かし評価

Can Go AIs be adversarially robust?

Authors: Tom Tseng, Euan McLean, Kellin Pelrine, Tony T. Wang, Adam Gleave | Published: 2024-06-18 | Updated: 2025-01-14
モデル性能評価
攻撃手法
透かし評価

Auditing Differential Privacy Guarantees Using Density Estimation

Authors: Antti Koskela, Jafar Mohammadi | Published: 2024-06-07 | Updated: 2024-10-11
プライバシー保護手法
評価手法
透かし評価

Towards Certification of Uncertainty Calibration under Adversarial Attacks

Authors: Cornelius Emde, Francesco Pinto, Thomas Lukasiewicz, Philip H. S. Torr, Adel Bibi | Published: 2024-05-22
評価手法
透かし評価
難易度キャリブレーション

Naturally Private Recommendations with Determinantal Point Processes

Authors: Jack Fitzsimons, Agustín Freitas Pasqualini, Robert Pisarczyk, Dmitrii Usynin | Published: 2024-05-22
ウォーターマーキング
プライバシー保護手法
透かし評価

WaterPool: A Watermark Mitigating Trade-offs among Imperceptibility, Efficacy and Robustness

Authors: Baizhou Huang, Xiaojun Wan | Published: 2024-05-22
ウォーターマーキング
透かしの耐久性
透かし評価

Fully Exploiting Every Real Sample: SuperPixel Sample Gradient Model Stealing

Authors: Yunlong Zhao, Xiaoheng Deng, Yijing Liu, Xinjun Pei, Jiazhi Xia, Wei Chen | Published: 2024-05-18
モデル性能評価
評価手法
透かし評価