We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classification (2D), and video classification (3D). The framework focuses on identifying sensitive regions and inducing misclassifications with minimal distortions and various distortion types. The novel RL method outperforms state-of-the-art methods for all three applications, proving its efficiency. Our RL approach produces superior localization masks, enhancing interpretability for image classification and ECG analysis models. For applications such as ECG analysis, our platform highlights critical ECG segments for clinicians while ensuring resilience against prevalent distortions. This comprehensive tool aims to bolster both resilience with adversarial training and transparency across varied applications and data types.
Black-box Attacks on Image Activity Prediction and its Natural Language Explanations
Idiap Research Institute（伊迪亚普研究所）；University of Perugia（佩鲁贾大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Queen Mary University of London（伦敦玛丽女王大学）
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
School of Computer Science, Peking University（北京大学计算机学院）；University of Pennsylvania（宾夕法尼亚大学）；Harvard University（哈佛大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification
Jagiellonian Human-Centered AI Lab, Mark Kac Center for Complex Systems Research（杰哥利昂人中心人工智能实验室、马克·卡复杂系统研究中心）；Jagiellonian University（杰哥利昂大学）；Department of Computer Science（计算机科学系）；Norwegian University of Science and Technology (NTNU)（挪威科学技术大学（NTNU））；Department of Human-Centered Artificial Intelligence（人中心人工智能系）；Institute of Applied Computer Science（应用计算机科学研究所）；School of Information Technology（信息科技学院）
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training
Tsinghua University（清华大学）；HeartVoice Medical Technology（心之声医疗科技）；University of Science and Technology of China（中国科学技术大学）；HeartRhythm Medical（心律医疗）；Tianjin Institute of Cardiology（天津心脏病学研究所）；Second Hospital of Tianjin Medical University（天津医科大学第二医院）；Peking University（北京大学）
ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification
Dipartimento di Informatica, Università di Verona, Verona, Italy（信息学院，威尼斯大学，威尼斯，意大利）；Department of Computer Science, School of Mathematics and Computer Science, Institute of Business Administration, Karachi, Pakistan（计算机科学系，数学与计算机科学学院，商学院，卡拉奇，巴基斯坦）；Faculty of Information Technology, Salim Habib University, Karachi, Pakistan（信息技术学院，萨利姆·哈比大学，卡拉奇，巴基斯坦）
Beyond Local Inspection: Global, Guideline-Grounded Evaluation of Post-hoc XAI Methods for ECG Classification
Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心（hessian.AI））；Technische Hochschule Mittelhessen, University of Applied Sciences（中黑森应用科学大学）；Justus-Liebig-University Giessen（吉森尤斯图斯-李比希大学）
Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification
School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（牛津大学工程科学系生物医学工程研究所）；School of Computer Science, University of Nottingham Ningbo China（宁波诺丁汉大学计算机科学学院）
Analysis of the Shortcut Learning and Clever Hans Effect in CNN based ECG Image Classification
Banaras Hindu University（贝拿勒斯印度教大学）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；University of Petroleum and Energy Studies（石油与能源研究大学）
How Well Do Multimodal Models Reason on ECG Signals?
University of Illinois Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）；Rush University（拉什大学）；ETH Zurich（苏黎世联邦理工学院）；St. Christopher's Hospital for Children（圣克里斯opher儿童医院）；Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；Google Inc（谷歌公司）
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals
Walkky（沃基公司）；Chobanian & Avedisian School of Medicine, Boston University（波士顿大学乔巴尼亚与阿夫迪西安医学院）；Clemson University（克莱姆森大学）；Northwell Health Physician Partners（诺斯韦尔健康医师伙伴）；Lenox Hill Hospital（莱诺克斯山医院）
Black-box Explanation of Object Detectors via Saliency Maps
Boston University（波士顿大学）；Adobe Research（Adobe研究院）；Adobe Document Cloud（Adobe文档云）；University of Virginia（弗吉尼亚大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
Saliency strikes back: How filtering out high frequencies improves white-box explanations
Carney Institute for Brain Science, Brown University（布朗大学卡尼脑科学研究所）；CerCo, CNRS（法国国家科学研究中心CerCo研究所）；SNCF（法国国家铁路公司）；Institute of Mathematics of Toulouse, University Paul Sabatier（保罗萨巴蒂埃大学图卢兹数学研究所）
Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification
Healthcare Technology and Innovation Center (HTIC), Indian Institute of Technology (IIT-M)（印度理工学院马德拉斯分校医疗技术与创新中心）；Indian Institute of Technology, Madras (IITM)（印度理工学院马德拉斯分校）
Center for the Philosophy of AI（哲学人工智能中心）；University of Copenhagen（哥本哈根大学）；Department of Philosophy and History of Ideas（哲学与思想史系）；Aarhus University（奥胡斯大学）
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
Carney Institute for Brain Science, Brown University（布朗大学卡尼脑科学研究所）；Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse（图卢兹大学图卢兹人工与自然智能研究所）；Institut de Recherche Technologique Saint-Exupery（圣埃克苏佩里技术研究所）；Sorbonne Université, CNRS（索邦大学、法国国家科学研究中心）
Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier
School of Computer Science, University College Dublin（都柏林大学学院计算机科学学院）；Insight Centre for Data Analytics（数据分析洞察中心）；VistaMilk SFI Research Centre（VistaMilk SFI研究中心）
