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

Treating a different kind of patient: curing security weaknesses in digital health systems of the future

Regazzoni, F., Palmieri, P., & Fournaris, A. P. (2023) · International Workshop on Advances in Sensors and Interfaces (IWASI) · doi:10.1109/iwasi58316.2023.10164581 · Cited by 2

Dialysis resource allocation in critical care: the impact of the COVID-19 pandemic and the promise of big data analytics

Koraishy, F. M., & Mallipattu, S. K. (2023) · Frontiers in Nephrology · doi:10.3389/fneph.2023.1266967 · Cited by 2

Towards Augmentation Based Defense Strategies Against Adversarial Attacks

Kharal, R. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00216 · Cited by 3

Optimal Feature Manipulation Attacks Against Linear Regression

Li, F., Lai, L., & Cui, S. R. (2022) · Wireless networks · doi:10.1007/978-3-031-16375-3_2 · Cited by 2

On the Adversarial Robustness of LASSO Based Feature Selection

Li, F., Lai, L., & Cui, S. R. (2022) · Wireless networks · doi:10.1007/978-3-031-16375-3_3 · Cited by 2

Applications of Machine Learning Algorithms in Cancer Diagnosis

Sharma, A., Jain, S., & Chatterjee, S. (2021) · Springer eBooks · doi:10.1007/978-981-16-0811-7_8 · Cited by 2

Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosis

Pchelintsev, Y. A., Khvostikov, A. V., Krylov, A. S., Parolina, L., Nikoforova, N. A., Lp, S., Prokop’ev, E. S., Farias, M. C. Q., & Yong, D. (2022) · Computational Mathematics and Modeling · doi:10.1007/s10598-023-09568-3 · Cited by 2

Patch augmentation: Towards efficient decision boundaries for neural networksPreprint

Bloice, M. D., Roth, P. M., & Holzinger, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.07922 · Cited by 3

Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imagingPreprint

Zech, J. R., Forde, J. Z., & Littman, M. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03606 · Cited by 4

MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based EnsemblesPreprint

Guo, Y., Hu, Q., Cordy, M., Papadakis, M., & Traon, Y. L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.12838 · Cited by 3

Quantifying Trustworthiness of Explainability in Medical AIPreprint

Zhang, J., Chao, H., Dasegowda, G., Wang, G., Kalra, M. K., & Yan, P. (2022) · Research Square · doi:10.21203/rs.3.rs-1897856/v1 · Cited by 2

Digital public health technologies: rationale for a multidisciplinary research laboratoryPreprint

Андрейченко, А. Е., Gorbacheva, A. M., Denisova, D., Okhotin, A., Kriuchkova, A., Pushkina, N., & Барчук, А. А. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/2kxbm · Cited by 2

Large Language Models Training, Challenges, Applications, and DevelopmentPreprint

Taghandiki, K., & Mohammadi, M. H. (2024) · doi:10.36227/techrxiv.171742381.10443150/v1 · Cited by 3

Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear featuresPreprint

Barman, R. K., Dhruba, S. R., Hoang, D.-T., Shulman, E. D., Campagnolo, E. M., Wang, A. T., Harmon, S. A., Hu, T. C., Papanicolau‐Sengos, A., Nasrallah, M. P., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.04.674077 · Cited by 2

Artificial intelligence in medicine: its working, potentials and challenges

Goel, R. (2022) · International Journal of Advances in Medicine · doi:10.18203/2349-3933.ijam20223412 · Cited by 2

Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training

Ali, A., Migliorati, A., Bianchi, T., & Magli, E. (2023) · EURASIP Journal on Information Security · doi:10.1186/s13635-023-00137-0 · Cited by 1

Application of ISO 14971 to machine learning in artificial intelligence—Guide

(2023) · doi:10.2345/9781570208669.ch1 · Cited by 1

AI in Dermatology: Bridging the Gap Between Potential and Practice

Klarycki, J., Tomczyk, K., Podgórska, D., Sanecki, M., Jurasz, K., Chojnacka, N., Rzeska, E., & Cymer, R. (2024) · Journal of Education Health and Sport · doi:10.12775/jehs.2024.52.004 · Cited by 1

Exploring biologically inspired mechanisms of adversarial robustness

Holzhausen, K., Merlid, M., Torvik, H. O., Malthe‐Sørenssen, A., & Lepperød, M. E. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11019-6 · Cited by 1

RobMedNAS: searching robust neural network architectures for medical image synthesis

Zhang, J., Chen, W., Joshi, T., Uyanik, M., Zhang, X., Loh, P., Jog, V., Bruce, R. J., Garrett, J. W., & McMillan, A. B. (2024) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ad6e87 · Cited by 1

Leveraging Machine Unlearning for Better Medical Care and Data Protection in Healthcare 6.0

Moses, S., Obermann, K., Gautam, K., Upadhyay, G., & Datla, J. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-7210-4.ch011 · Cited by 1

Chronic Disease Symptom Checker with NLP

Vinoth Kumar, P., Arul Selvam, N., & Soundharajan, P. (2025) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl65848.2025.10933199 · Cited by 1

Artificial Intelligence in Healthcare: Navigating Security and Privacy Challenges

Çela, E., Fonkam, M. M., Vedishchev, A., & Vajjhala, N. R. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-84404-1_1 · Cited by 3

Reducing the Carbon Footprint in Healthcare Cybersecurity Threats in AI-Powered Healthcare Systems Data Privacy in AI-Driven Diagnostics

Adeyinka, T. I., & Adeyinka, K. I. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2827-0.ch011 · Cited by 1

Evidential Reasoning Rule Learning

Zhou, Z., & Liu, H. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3569495 · Cited by 1

MLFD: Multi-level feature disentanglement robust network with a novel maximum-minimum algorithm for medical image recognition

Yuan, J., Wu, F., Zhao, L., & Kang, Q. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128815 · Cited by 1

Introduction to block chain technology and potential applications in vascular surgery

Wilkinson, M. C., Roh, D., Kiang, S. C., & Tomihama, R. T. (2025) · JVS-Vascular Insights · doi:10.1016/j.jvsvi.2025.100262 · Cited by 1

Robust Deep Learning Model for Adversarial Defense in Medical Image Diagnosis

Zahoor, K., & Ghani, U. (2025) · International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC) · doi:10.1109/icetecc65365.2025.11070282 · Cited by 1

Parameter-Efficient Multimodal Adaptation for Certified Robustness of Medical Vision-Language Models

Shamshad, F., Hussein, N., & Nandakumar, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98688-8_21 · Cited by 1

Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks

Liew, A., & Agaian, S. С. (2025) · Bioengineering · doi:10.3390/bioengineering12090914 · Cited by 1

AI in Facial Cosmetic Surgery: Surgeon’s Perspective Summary

Hamrah, D., Eidson, B. K., & Vargas, N. (2025) · The American Journal of Cosmetic Surgery · doi:10.1177/07488068251343281 · Cited by 1

Towards building robust models for unimodal and multimodal medical imaging data

Dhar, J., Goyal, P., Haghighat, M., Zaidi, N. A., Sohel, F. A., Vo, Q. B., & Santosh, K. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103822 · Cited by 1

Dual adversarial attacks on Explainable Deep Learning in medical image classification

Li, W., & Zainon, W. M. N. W. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109125 · Cited by 1

The Robustness of Deep Learning Models to Adversarial Attacks in Lung X-ray Classification

Li, X., Pang, Y., & Li, Y. (2025) · International Conference on Machine Learning, Pattern Recognition and Automation Engineering (MLPRAE) · doi:10.1109/mlprae67267.2025.11290762 · Cited by 3

SPARTA: Spectral Prompt Agnostic Adversarial Attack on Medical Vision-Language Models

Hanif, A., Zaheer, Z., Khan, S., Khan, F. S., & Anwer, R. M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06593-3_7 · Cited by 1

Am (A)I hallucinating? Nonrobustness, hallucinations, and unpredictable performance of AI for MR image reconstruction

Antun, V., Gottschling, N. M., Hansen, A. C., & Adcock, B. (2025) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-443-14109-6.00011-0 · Cited by 1

Systems-Theoretic and Data-Driven Security Analysis in ML-Enabled Medical Devices

Mitra, G., Hallajiyan, M., Kim, I. L., Dharmalingam, A. P., Elnawawy, M., Iqbal, S., Pattabiraman, K., & Alemzadeh, H. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-13800-2_11 · Cited by 1

CARDIO-AI: Compliance and artificial intelligence regulation for deep learning in electrocardiogram and cardiac signal interpretation

Rajabi, M. T., Fazilati, F., Alihosseini, N., Farsani, M. E., Biriaei, F., Farahani, M. A., Khorasgani, A., Sadeghipour, F., Marateb, H. R., & Mañanas, M. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45242-0.00007-4 · Cited by 1

Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks

Soltani, N., Nejadshamsi, S., Houda, Z. A. E., Khoury, R., Costa, K. A. P., Falk, T. H., & Avila, A. R. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342479 · Cited by 1

FGSM Based Adversarial Attacks in Robust Machine Learning: A Review

Benchaira, D., & Cherif, F. (2025) · International Conference on Research in Computing at Feminine (RIF) · doi:10.1109/rif68108.2025.11406808 · Cited by 1

A Secure and Robust ML Framework for Sequence Classification and Adversarial Evaluation in a Bilateral Carpal Tunnel Syndrome Crossover Dataset

Kharat, P. G., Majmaie, S. A., Ghajari, G., Amsaad, F., & Ibrahem, M. I. (2026) · Information · doi:10.3390/info17030293 · Cited by 1

A Smart AI-Driven Diagnostic Framework for Early Detection of Skin Cancer Using Hybrid Vision Transformers and Cloud-Enabled mHealth Integration.

Reddy, N. N., & Agarwal, P. R. (2025) · doi:10.1145/3783862.3783885 · Cited by 1

Instrumentation Digital Twins in PET and SPECT Imaging: Current Status, Challenges, and Future Directions

Azimi, M.-S., Karakatsanis, N. A., Sabet, H., Rafecas, M., Nehmeh, S. A., Abass, A. S., King, M. A., & Zaidi, H. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0046 · Cited by 1

Integration of digital physicalomics and dual-fluid metabolomics flux ratios reveals tubular secretory dysfunction in early diabetic kidney disease

Zhang, H., Cai, Q., Gao, J., Wang, F., Zhang, L., Zhao, H., Ren, X., & Zhang, M. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1802447 · Cited by 2

Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review

Rai, M. C. E., Far, A. B., Darweesh, M., Dhou, S., Aburaed, N., El, S., Elkhazendar, M., & Ellahham, S. (2026) · Diagnostics · doi:10.3390/diagnostics16101485 · Cited by 1

Protection of the Information System of the Printing Enterprise from Cyber Threats

Durnyak, B., Shepita, P., & Tupychak, L. (2023) · International Workshop on Intelligent Information Technologies & Systems of Information Security (IntelITSIS) · doi:10.55056/ceur-ws.org/vol-3373/paper31.pdf · Cited by 1

Navigating AI and machine learning in cancer research: an end-to-end translational framework

Saha, S., Ali, M. S., Tengli, A. K., Prasad, S. R., Mallikarjunaswamy, P., Pillappan, R., & Javarappa, K. K. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-08503-5 · Cited by 3

Medical question answering: A comprehensive multimodal and LLM-driven survey

Mhedhbi, E., Zhu, X., Ayaz, M., Khan, M., & Othmani, A. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109541 · Cited by 1

A simulation study of differentially private federated learning with zero-trust policy screening for medical IoT devices

Masad, I. S., Alqudah, A. M., Qazan, S., & Shoaib, S. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1807342 · Cited by 1

Artificial Intelligence in Healthcare: From Predictive Models to Generative, Multimodal, and Agentic AI

BAYDİLİ, I., Taşçı, B., Taşçı, G., Doğan, Ş., & Tuncer, T. (2026) · Bioengineering · doi:10.3390/bioengineering13101175 · Cited by 1

Scientific progress and clinical uncertainty

Tavazzi, L. (2020) · European Heart Journal Supplements · doi:10.1093/eurheartj/suaa155 · Cited by 1

Hybrid Cardiac Imaging: The Role of Machine Learning and Artificial Intelligence

Benjamins, J. W., Yeung, M. W., Reyes-Quintero, A. E., Ruijsink, B., van der Harst, P., & Juárez‐Orozco, L. E. (2022) · Springer eBooks · doi:10.1007/978-3-030-99391-7_12 · Cited by 1

Right to Life, Liberty and Security of Persons

Stahl, B. C., Schroeder, D., & Rodrigues, R. (2022) · SpringerBriefs in research and innovation governance · doi:10.1007/978-3-031-17040-9_6 · Cited by 1

Sodobne perspektive družbe: Umetna inteligenca na stičišču znanosti

Bregant, J., Aberšek, B., & Borstner, B. (2022) · doi:10.18690/um.ff.11.2022 · Cited by 8

An Adversarial Sample Defense Method Based on Saliency Information

Liu, S., Zhuang, Y., Ma, X., Wang, H., & Cao, D. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-0272-9_21 · Cited by 1

THE TECHNIQUE OF BUILDING A SUSTAINABLE PROTECTION SYSTEM BASED ON ADVERSARIAL MACHINE LEARNING IN 6G WIRELESS NETWORKS

Legashev, L. V., & Grishina, L. (2023) · Voprosy kiberbezopasnosti · doi:10.21681/2311-3456-2023-2-99-108 · Cited by 1

Boosting Adversarial Training in Adversarial Machine Learning

Saleh, E., Poguda, A. A., & Shashev, D. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-30648-8_7 · Cited by 1

Artificial Intelligence and Teledermatology

Fernandez, K., Young, A. T., Bhattarcharya, A., Kusari, A., & Wei, M. L. (2023) · Updates in clinical dermatology · doi:10.1007/978-3-031-27276-9_18 · Cited by 1

DBA: downsampling-based adversarial attack in medical image analysis

Wang, Z., Zhang, S., Li, Y., & Pan, Q. (2023) · International Conference on Computer Vision and Pattern Analysis (ICCPA) · doi:10.1117/12.2684368 · Cited by 1

Data sharing principles

Reddy, C. D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90534-3.00050-0 · Cited by 1

Learning Activation Functions for Adversarial Attack Resilience in CNNs

Salimi, M., Loni, M., & Sirjani, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42505-9_18 · Cited by 1

Gradient-Based Enhancement Attacks in Biomedical Machine Learning

Rosenblatt, M., Dadashkarimi, J., & Scheinost, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45249-9_29 · Cited by 4

Exploiting the Divergence Between Output of ML Models to Detect Adversarial Attacks in Streaming IoT Applications

Albaseer, A. M., Abdallah, M. M., & Al‐Fuqaha, A. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278872 · Cited by 1

Parameter Identification for Fictitious Play Algorithm in Repeated Games

Dong, H., & Mu, Y. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-7590-7_22 · Cited by 1

Defense Method against Adversarial Attacks Using JPEG Compression and One-Pixel Attack for Improved Dataset Security

Cucu, A.-V., Valenzise, G., Stănescu, D., Ghergulescu, I., Găină, L. I., & Gușiță, B. (2023) · International Conference on System Theory, Control and Computing (ICSTCC) · doi:10.1109/icstcc59206.2023.10308520 · Cited by 1

Enhancing Branch and Bound for Robustness Verification of Neural Networks via an Effective Branching Strategy

Han, S., & Zhang, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8664-4_3 · Cited by 1

The growing threat of cyberwarfare in cancer healthcare

Gruber, K. (2023) · Nature Cancer · doi:10.1038/s43018-023-00659-z · Cited by 1

The Influence of Teacher Professional Competence on Student Learning Motivation in PAI Subjects

Arrobi, J., Supiani, E., & Lestari, S. R. (2023) · Al-Hijr Journal of Adulearn World · doi:10.55849/alhijr.v2i3.559 · Cited by 2

Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach

Lee, W., & Kim, Y. (2024) · Applied Sciences · doi:10.3390/app14052130 · Cited by 1

On One-Pixel Attacking Medical Images against Deep Learning Models

Wang, Y., Wang, W., Wang, X., & Chen, Z. (2023) · International Symposium on Artificial Intelligence for Medicine Sciences (ISAIMS) · doi:10.1145/3644116.3644161 · Cited by 1

Artificial Intelligence in Cutaneous Lesions: Where do we Stand and What is Next?

(2024) · Frontiers research topics · doi:10.3389/978-2-8325-4911-7 · Cited by 1

Making Domain Specific Adversarial Attacks for Retinal Fundus Images

Joseph, N., Ameer, P. M., George, S. N., & Raja, K. B. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-58535-7_24 · Cited by 1

Transformative AI in Biomedicine Analysis

Tiwari, N. K. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-9311-6.ch006 · Cited by 1

Addressing Current and Emerging Challenges in DF: An Examination and Recommendations

Cho, A., & Montasari, R. (2024) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-031-72821-1_3 · Cited by 1

Machine learning and pathology: a historical perspective

Malpani, S., Paz, R., Mirzabeigi, Y., Mohammadi, S. M. K., Gasca, R., & Arora, H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15504-8.00002-8 · Cited by 1

Discriminator to grade classification results of neural networks via Prediction Error Tracking Method

Lee, G., Yun, C., Kim, S.-J., & Kang, B. B. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.112954 · Cited by 1

Enhancing security in WBANs: novel multi-vibrate time series analysis for adversarial attack prediction in intensive care settings

Kalpana, R. N. L. S., Patro, A. K., & Rao, D. N. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02703-2 · Cited by 1

Securing 3D Deep Learning Models: Simple and Effective Defense Against Adversarial Attacks

Kharal, R., Ali, S. A.-D., Mohiuddin, U., & Sayed, S. E. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00234 · Cited by 1

Ethical, Regulatory, and Practical Challenges in Artificial Intelligence-Driven Chest Imaging

de Lima, J. B. C., Griffin, I., Gemmell, J. S., Davis, K., Amanamba, U., Zanjani, N. A., Siyanaki, M. R. H., Mohammed, T.-L. H., Benos, T., Rodrigues, R. S., et al. (2025) · Seminars in Roentgenology · doi:10.1053/j.ro.2025.06.003 · Cited by 2

Artificial Intelligence in Ophthalmology: Promises, Hazards and Challenges

Grzybowski, A. E. (2025) · Springer eBooks · doi:10.1007/978-3-031-83756-2_1 · Cited by 1

Expanding contingency learning models of aggression in youth: Conceptual and methodological considerations to enhance clinical utility

Byrd, A. L., Kahhalé, I., Vize, C. E., Griffith, R., Viding, E. M., & Stepp, S. D. (2025) · Aggression and Violent Behavior · doi:10.1016/j.avb.2025.102082 · Cited by 1

Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification

Ali, A., Migliorati, A., Bianchi, T., & Magli, E. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412277 · Cited by 1

Early detection and diagnosis using deep learning

Sharma, D., Bhargava, S., Jha, A., & Singh, P. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823014-5.00001-6 · Cited by 1

Robust Certification for Laplace Learning on Geometric Graphs

Thorpe, M., & Bao, W. (2021) · Research Explorer (The University of Manchester) · doi:10.48550/arxiv.2104.10837 · Cited by 1

From Compressed Sensing to Deep Learning

Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.023 · Cited by 1

Compressed Sensing for Imaging

Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.019 · Cited by 1

A Data-Free Approach for Targeted Universal Adversarial Perturbation

Wang, X., Bai, T., & Zhao, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89137-4_9 · Cited by 1

The LASSO and its Cousins

Adcock, B., & Hansen, A. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108377447.009 · Cited by 1

On Effectiveness of the Adversarial Attacks on the Computer Systems of Biomedical Images Classification

Shchetinin, E. Y., Glushkova, A. G., & Blinkov, Y. A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-30648-8_8 · Cited by 1

Local Competition and Uncertainty for Adversarial Robustness in Deep LearningPreprint

Alexos, A., Panousis, K. P., & Chatzis, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10620 · Cited by 1

When Bots Take Over the Stock Market: Evasion Attacks Against Algorithmic Traders.Preprint

Nehemya, E., Mathov, Y., Shabtai, A., & Elovici, Y. (2020) · arXiv (Cornell University) · Cited by 1

Adversarial Interaction Attack: Fooling AI to Misinterpret Human IntentionsPreprint

Koren, N., Ke, Q., Wang, Y., Bailey, J., & Ma, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.06704 · Cited by 1

Stabilizing Deep Tomographic ReconstructionPreprint

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Reply to “Malicious Adversarial Attacks on Medical Image Analysis”

Desjardins, B., & Ritenour, E. R. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.24244

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