Adversarial attacks on medical machine learning

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310

End-To-End Arguments In System Design

And, S. R., Saltzer, J. H., Reed, D. P., & Clark, D. D. (1984) · Cited by 2,130

Wild patterns: Ten years after the rise of adversarial machine learning

Biggio, B., & Roli, F. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.023 · Cited by 1,563

Robustness May Be at Odds with AccuracyPreprint

Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., & Mądry, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12152 · Cited by 1,513

The Future of the Internet--And How to Stop It

Zittrain, J. L. (2008) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 1,371

Untitled work

(2017) · doi:10.48550/arxiv.1712.02779 · Cited by 300

Adversarial Attacks Against Medical Deep Learning SystemsPreprint

Finlayson, S. G., Chung, H. W., Kohane, I. S., & Beam, A. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.05296 · Cited by 289

Physician manipulation of reimbursement rules for patients: between a rock and a hard place.

Wynia, M. K., Cummins, D. S., VanGeest, J. B., & Wilson, I. B. (2000) · PubMed · doi:10.1001/jama.283.14.1858 · Cited by 238

Corruption: medicine's dirty open secret

Jain, A., Nundy, S., & Abbasi, K. A. (2014) · BMJ · doi:10.1136/bmj.g4184 · Cited by 69

Overbilling vs. Downcoding — The Battle between Physicians and Insurers

Kesselheim, A. S., & Brennan, T. A. (2005) · New England Journal of Medicine · doi:10.1056/nejmp058011 · Cited by 26

Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation

Zadorozhny, K., Thoral, P. J., Elbers, P., & Cinà, G. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-14771-5_10 · Cited by 22

Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models

Xiao, J., Tian, Y., Jia, Y., Jiang, X., Yu, L., & Wang, S. (2023) · INFORMS journal on computing · doi:10.1287/ijoc.2023.1297 · Cited by 20

Implementing Artificial Intelligence

Caruso, P. F., Greco, M., Ebm, C. C., Angelotti, G., & Cecconi, M. (2023) · Critical Care Clinics · doi:10.1016/j.ccc.2023.03.007 · Cited by 21

Towards Building More Robust Models with Frequency Bias

Bu, Q., Huang, D., & Cui, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00406 · Cited by 20

Challenges and Opportunities for AI in Healthcare

Kan, Y. (2024) · International Journal of Law and Policy · doi:10.59022/ijlp.203 · Cited by 26

When Deep Learning Meets Polyhedral Theory: A Survey

Huchette, J., Muñoz, G., Serra, T., & Tsay, C. (2026) · INFORMS journal on computing · doi:10.1287/ijoc.2024.0902 · Cited by 71

DEAttack: A differential evolution based attack method for the robustness evaluation of medical image segmentation

Xiangxiang, C., Chang, S., Li, C., Kong, B., Tian, L., Wang, H., Huang, P. F., Yang, M., Wu, Y., & Li, Z. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.118 · Cited by 20

Applications of machine learning approaches to combat COVID-19: A survey

Tiwari, S. M., Doğan, O., Jabbar, M. A., Shandilya, S. K., Ortiz, F., Bajpai, S., & Banerjee, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99878-9.00014-5 · Cited by 19

Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives

Selvakkumar, A., Pal, S., & Jadidi, Z. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-98886-9_21 · Cited by 22

Overlooked Trustworthiness of Saliency Maps

Zhang, J., Chao, H., Dasegowda, G., Wang, G., Kalra, M. K., & Yan, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_43 · Cited by 19

Large language models and the emergence phenomena

Sorin, V., & Klang, E. (2023) · European Journal of Radiology Open · doi:10.1016/j.ejro.2023.100494 · Cited by 20

TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization

Liu, Z., Xu, Y. T., Ji, X., & Chan, A. B. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01577 · Cited by 20

Artificial Intelligence/Machine Learning: The New Frontier of Clinical Pharmacology and Precision Medicine

Liu, Q., Joshi, A., Standing, J. F., & van der Graaf, P. H. (2024) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3198 · Cited by 20

Trustworthy machine learning in the context of security and privacy

Upreti, R., Lind, P. G., Elmokashfi, A. M., & Yazidi, A. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00813-3 · Cited by 25

Neural Networks and Deep Learning

Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.024 · Cited by 19

Simple Black-Box Universal Adversarial Attacks on Deep Neural Networks for Medical Image Classification

Koga, K., & Takemoto, K. (2022) · Algorithms · doi:10.3390/a15050144 · Cited by 20

Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges

Hatherley, J. J., & Sparrow, R. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac218 · Cited by 18

QuoTe : Quality-oriented Testing for Deep Learning Systems

Chen, J., Wang, J., Ma, X., Sun, Y., Sun, J., Zhang, P., & Cheng, P. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3582573 · Cited by 21

Seed Selection for Testing Deep Neural Networks

Zhi, Y., Xie, X., Shen, C. Q., Sun, J., Zhang, X., & Guan, X. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3607190 · Cited by 20

Versatile Weight Attack via Flipping Limited Bits

Bai, J., Wu, B., Li, Z., & Xia, S. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3296408 · Cited by 21

Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

Abidi, M. H., Alkhalefah, H., & Aboudaif, M. K. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.044169 · Cited by 18

From Drops to Decisions: AI/ML-Driven Biofluidics for Clinical Diagnostics and Healthcare Intelligence

Deshmukh, M. T., Thakre, Y., Bisen, W. H., Sankar, K., Avthankar, A., Shahade, A. K., Bhaiyya, M. L., & Kulkarni, M. B. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c06356 · Cited by 19

The feasibility and inevitability of stealth attacks

Tyukin, I., Higham, D. J., Bastounis, A., Woldegeorgis, E., & Gorban, A. N. (2023) · IMA Journal of Applied Mathematics · doi:10.1093/imamat/hxad027 · Cited by 18

Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges

Harish, K. B., Price, W. N., & Aphinyanaphongs, Y. (2022) · JMIR Formative Research · doi:10.2196/33970 · Cited by 17

Is there a civic duty to support medical AI development by sharing electronic health records?

Müller, S. (2022) · BMC Medical Ethics · doi:10.1186/s12910-022-00871-z · Cited by 17

Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks Using a Malware Dataset: A Comparative Analysis

Akter, M. S., Shahriar, H., Iqbal, I., Hossain, M., Karim, M. A., Clincy, V. A., & Voicu, R. (2023) · IEEE International Conference on Software Services Engineering (SSE) · doi:10.1109/sse60056.2023.00037 · Cited by 17

Principles of artificial intelligence and its application in cardiovascular medicine

Wieneke, H., & Voigt, I. (2023) · Clinical Cardiology · doi:10.1002/clc.24148 · Cited by 20

Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis

Prezja, F., Annala, L., Kiiskinen, S., & Ojala, T. (2023) · Algorithms · doi:10.3390/a17010008 · Cited by 18

On the Adversarial Robustness of Subspace Learning

Li, F., Lai, L., & Cui, S. R. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2974676 · Cited by 17

On the Adversarial Robustness of Hypothesis Testing

Jin, Y., & Lai, L. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3045206 · Cited by 17

Connectome-based machine learning models are vulnerable to subtle data manipulations

Rosenblatt, M., Rodriguez, R. X., Westwater, M. L., Dai, W., Horien, C. L., Greene, A. S., Constable, R. T., Noble, S., & Scheinost, D. (2023) · Patterns · doi:10.1016/j.patter.2023.100756 · Cited by 18

Marking the Path Toward Artificial Intelligence–Based Image Classification in Dermatology

Novoa, R. A., Gevaert, O., & Ko, J. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.1633 · Cited by 18

Artificial Intelligence You Can Trust: What Matters Beyond Performance When Applying Artificial Intelligence to Renal Histopathology?

Ayorinde, J., Citterio, F., Landrò, M., Peruzzo, E., Islam, T., Tilley, S., Taylor, G., Bardsley, V., Lió, P., Samoshkin, A., et al. (2022) · Journal of the American Society of Nephrology · doi:10.1681/asn.2022010069 · Cited by 16

Can We Revitalize Interventional Healthcare with AI-XR Surgical Metaverses?

Qayyum, A., Bilal, M., Hadi, M., Capik, P., Caputo, M., Vohra, H. A., Al‐Fuqaha, A., & Qadir, J. (2023) · IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom) · doi:10.1109/metacom57706.2023.00091 · Cited by 16

A secure blockchain framework for the internet of medical things

Sharma, A., Kaur, S., & Singh, M. (2023) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4917 · Cited by 18

Threat Modeling AI/ML With the Attack Tree

Hoseini, S. V., Suutala, J., Partala, J., & Halunen, K. (2024) · IEEE Access · doi:10.1109/access.2024.3497011 · Cited by 22

Intra-section code cave injection for adversarial evasion attacks on windows PE malware file

Aryal, K., Gupta, M., Abdelsalam, M., & Saleh, M. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104690 · Cited by 16

A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma

Lü, L., Wang, D., Wang, L., Linning, E., Guo, P., Li, Z., Xiang, J., Yang, H., Li, H., Yin, S., et al. (2020) · European Radiology · doi:10.1007/s00330-020-06663-6 · Cited by 16

Sparse oblique decision trees: a tool to understand and manipulate neural net features

Hada, S. S., Carreira-Perpiñán, M. Á., & Zharmagambetov, A. S. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00892-7 · Cited by 21

Artificial intelligence in healthcare—the road to precision medicine

Tran, T., du Toit, C., & Padmanabhan, S. (2021) · Journal of Hospital Management and Health Policy · doi:10.21037/jhmhp-20-132 · Cited by 17

Ocular Imaging Standardization for Artificial Intelligence Applications in Ophthalmology: the Joint Position Statement and Recommendations From the Asia-Pacific Academy of Ophthalmology and the Asia-Pacific Ocular Imaging Society

Ting, D. S. W., Wong, T. Y., Park, K. H., Cheung, C. Y.-L., Tham, C. C. Y., & Lam, D. S. C. (2021) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000421 · Cited by 16

The mathematics of adversarial attacks in AI – why deep learning is unstable despite the existence of stable neural networks

Bastounis, A., Hansen, A. C., & Vlačić, V. (2025) · European Journal of Applied Mathematics · doi:10.1017/s0956792525100193 · Cited by 44

Internet of Things, Machine Learning, and Blockchain Technology: Emerging technologies revolutionizing Universal Health Coverage

Babatunde, A. O., Togunwa, T. O., Awosiku, O. V., Siddiqui, M. F., Rabiu, A. T., Akintola, A. A., Dauda, B. J., & Aborode, A. T. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1024203 · Cited by 17

Artificial Intelligence in Breast X-Ray Imaging

Vedantham, S., Shazeeb, M. S., Chiang, J. A., & Vijayaraghavan, G. R. (2022) · Seminars in Ultrasound CT and MRI · doi:10.1053/j.sult.2022.12.002 · Cited by 17

Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical ChallengesPreprint

Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2020) · arXiv.org · doi:10.48550/arxiv.2012.09110 · Cited by 20

Ethics of AI and Health Care: Towards a Substantive Human Rights Framework

Liao, S. M. (2023) · Topoi · doi:10.1007/s11245-023-09911-8 · Cited by 17

Bridging Artificial Intelligence and Medical Education: Navigating the Alignment Paradox

Turner, L., Knopp, M. I., Mendonça, E. A., & Desai, S. V. (2025) · ATS Scholar · doi:10.34197/ats-scholar.2024-0086ps · Cited by 17

Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain

Marulli, F., Marrone, S., & Verde, L. (2022) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan11020021 · Cited by 15

Adversarial Medical Image With Hierarchical Feature Hiding

Yao, Q., He, Z., Li, Y., Lin, Y., Ma, K., Zheng, Y., & Zhou, S. K. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3335098 · Cited by 17

Professionalism in artificial intelligence: The link between technology and ethics

Klarin, A., Abadi, H. A., & Sharmelly, R. (2024) · Systems Research and Behavioral Science · doi:10.1002/sres.2994 · Cited by 28

A Survey of Trustworthy Representation Learning Across Domains

Zhu, R., Guo, D., Qi, D., Chu, Z., Yu, X., & Li, S. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3657301 · Cited by 25

Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images

Matsuo, Y., & Takemoto, K. (2021) · Applied Sciences · doi:10.3390/app11209556 · Cited by 16

Recommendations for machine learning benchmarks in neuroimaging

Leenings, R., Winter, N. R., Dannlowski, U., & Hahn, T. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119298 · Cited by 17

Predicting the prevalence of lung cancer using feature transformation techniques

Munawar, Z., Ahmad, F., Alanazi, S., Nisar, K. S., Khalid, M., Anwar, M., & Murtaza, K. (2022) · Egyptian Informatics Journal · doi:10.1016/j.eij.2022.08.002 · Cited by 17

What do AI algorithms actually learn? - On false structures in deep learningPreprint

Thesing, L., Antun, V., & Hansen, A. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01478 · Cited by 15

Sensitivity of neural networks to corruption of image classification

Kaplan, S., Handelman, D., & Handelman, A. (2021) · AI and Ethics · doi:10.1007/s43681-021-00049-0 · Cited by 14

AN-GCN: An Anonymous Graph Convolutional Network Against Edge-Perturbing Attacks

Liu, A., Li, B., Li, T., Zhou, P., & Wang, R. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3172296 · Cited by 18

Towards Robustifying Image Classifiers against the Perils of Adversarial Attacks on Artificial Intelligence Systems

Anastasiou, T., Καραγιώργου, Σ., Petrou, P., Papamartzivanos, D., Giannetsos, T., Tsirigotaki, G., & Keizer, J. (2022) · Sensors · doi:10.3390/s22186905 · Cited by 14

Should AI-Enabled Medical Devices be Explainable?

Matulionytė, R., Nolan, P., Magrabi, F., & Beheshti, A. (2022) · International Journal of Law and Information Technology · doi:10.1093/ijlit/eaac015 · Cited by 14

Challenges of Machine Learning and AI (What Is Next?), Responsible and Ethical AI

Gkontra, P., Quaglio, G., Garmendia, A. T., & Lekadir, K. (2023) · Springer eBooks · doi:10.1007/978-3-031-36678-9_17 · Cited by 17

SPY-Watermark: Robust Invisible Watermarking for Backdoor Attack

Wang, R., Wan, R., Guo, Z., Guo, Q., & Huang, R. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448363 · Cited by 15

Integration of Federated Learning and AI-Generated Content: A Survey of Overview, Opportunities, Challenges, and Solutions

Liu, Y., Yin, J., Zhang, W., An, C., Xia, Y., & Zhang, H. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3523350 · Cited by 17

An online platform for interactive feedback in biomedical machine learning

Abid, A., Ali, A., Abid, A., Khan, D., Alfozan, A., & Zou, J. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0147-8 · Cited by 14

Assignment flows for data labeling on graphs: convergence and stability

Zern, A., Zeilmann, A., & Schnörr, C. (2021) · Information Geometry · doi:10.1007/s41884-021-00060-8 · Cited by 14

Management at the Edge of Situation Awareness During Patient Telemonitoring

Ardito, C. A., Noia, T. D., Fasciano, C., Lofù, D., Macchiarulo, N., Mallardi, G., Pazienza, A., & Vitulano, F. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77091-4_23 · Cited by 14

Color-Optimized One-Pixel Attack Against Digital Pathology Images

Korpihalkola, J., Sipola, T., & Kokkonen, T. (2021) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct52173.2021.9435562 · Cited by 14

The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence

Watson, D. (2020) · Digital ethics lab yearbook · doi:10.1007/978-3-030-29145-7_4 · Cited by 15

DeepCAD: A Stand-alone Deep Neural Network-based Framework for Classification and Anomaly Detection in Smart Healthcare Systems

Haque, N. I., Rahman, M. A., & Ahamed, S. I. (2022) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh55609.2022.00042 · Cited by 14

Adversarial attacks and adversarial robustness in computational pathologyPreprint

Laleh, N. G., Truhn, D., Veldhuizen, G. P., Han, T., van Treeck, M., Buelow, R. D., Langer, R., Dislich, B., Boor, P., Schulz, V., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.15.484515 · Cited by 14

A multi-layered defense against adversarial attacks in brain tumor classification using ensemble adversarial training and feature squeezing

Yinusa, A., & Faezipour, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-00890-x · Cited by 13

Sociotechnical challenges to the technological accuracy of computer vision: The new materialism perspective

Kim, E. S., Oh, Y., & Yun, G. W. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102388 · Cited by 20

Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges

de Aguiar, E. J., Traina, C., & Traina, A. J. M. (2023) · Yearbook of Medical Informatics · doi:10.1055/s-0043-1768731 · Cited by 20

Synergies between machine learning and reasoning - An introduction by the Kay R. Amel group

Baaj, I., Bouraoui, Z., Cornuéjols, A., Denœux, T., Destercke, S., Didier Dubois, A., Lesot, M., Marques-Silva, J. P., Mengin, J., Prade, H. M., et al. (2024) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2024.109206 · Cited by 13

Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems

Elnawawy, M., Hallajiyan, M., Mitra, G., Iqbal, S., & Pattabiraman, K. (2024) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase60773.2024.00019 · Cited by 15

AI Under Attack: Metric-Driven Analysis of Cybersecurity Threats in Deep Learning Models for Healthcare Applications

Brohi, S. N., & Mastoi, Q.-U.-A. (2025) · Algorithms · doi:10.3390/a18030157 · Cited by 13

DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting

Chen, S., Wong, E., Kolter, J. Z., & Fazlyab, M. (2022) · IEEE Open Journal of Control Systems · doi:10.1109/ojcsys.2022.3187429 · Cited by 13

Novel-view X-ray projection synthesis through geometry-integrated deep learning

Shen, L., Yu, L., Zhao, W., Pauly, J. M., & Xing, L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102372 · Cited by 13

Rebirth of Distributed AI—A Review of eHealth Research

Khan, M. A., & Alkaabi, N. (2021) · Sensors · doi:10.3390/s21154999 · Cited by 13

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions.

Osuala, R. O., Kushibar, K., Garrucho, L., Linardos, A., Szafranowska, Z., Klein, S., Glocker, B., Díaz, O., & Lekadir, K. (2021) · Spiral (Imperial College London) · Cited by 13

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research DirectionsPreprint

Alsmadi, I. M., Ahmad, K., Nazzal, M., Alam, F., Al‐Fuqaha, A., Khreishah, A. A., & Algosaibi, A. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.13980 · Cited by 13

Hallucinations in medical devices

Granstedt, J., Prabhat, K. C., Deshpande, R., Garcia, V. L., & Badano, A. G. (2025) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2025.100145 · Cited by 18

Mitigating adversarial evasion attacks by deep active learning for medical image classification

Ahmed, U., Lin, J. C., & Srivastava, G. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11473-z · Cited by 13

Pruning Adversarially Robust Neural Networks without Adversarial Examples

Jian, T., Wang, Z., Wang, Y., Dy, J., & Ioannidis, S. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00120 · Cited by 14

Query-Efficient Black-Box Adversarial Attacks on Automatic Speech Recognition

Tong, C., Zheng, X., Li, J., Ma, X., Gao, L., & Xiang, Y. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3304476 · Cited by 12

Physical Backdoor: Towards Temperature-Based Backdoor Attacks in the Physical World

Yin, W., Lou, J., Zhou, P., Xie, Y., Feng, D., Sun, Y., Zhang, T., & Sun, L. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01210 · Cited by 12

Securing Cloud AI Workloads: Protecting Generative AI Models from Adversarial Attacks

Patel, A., Pandey, P., Ragothaman, H., Molleti, R., & Tanikonda, A. (2025) · IEEE International Conference on AI in Cybersecurity (ICAIC) · doi:10.1109/icaic63015.2025.10848877 · Cited by 12

Explainable AI for Medical Imaging: Knowledge Matters

Bourdon, P., Ahmed, O. B., Urruty, T., Djemal, K., & Fernández-Maloigne, C. (2012) · Springer eBooks · doi:10.1007/978-3-030-74478-6_11 · Cited by 14

Unconventional Concerns for Human-Centered Artificial Intelligence

Cheruvu, R. (2022) · Computer · doi:10.1109/mc.2022.3170423 · Cited by 12

Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging

Joel, M. Z., Avesta, A., Yang, D. X., Zhou, J., Omuro, A. M. P., Herbst, R. S., Krumholz, H. M., & Aneja, S. (2023) · Cancers · doi:10.3390/cancers15051548 · Cited by 15

Implementation and design of artificial intelligence in abdominal imaging

Choi, H. H., Chang, S. D., & Kohli, M. D. (2020) · Abdominal Radiology · doi:10.1007/s00261-020-02471-0 · Cited by 13

What Are Machine and Deep Learning?

Naqa, I. E., & Murphy, M. J. (2022) · Springer eBooks · doi:10.1007/978-3-030-83047-2_1 · Cited by 18

Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology

Mehrtabar, S., Marey, A., Desai, A., Saad, A. M., Desai, V. S., Goñi, J., Pal, B., & Umair, M. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01656-7 · Cited by 14

Socialni odnosi, animizem, antropomorfizem in interakcija z UI

Babić, N. Č. (2022) · doi:10.18690/um.ff.11.2022.12 · Cited by 10

Stealthy dynamic backdoor attack against neural networks for image classification

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