DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

ImageNet classification with deep convolutional neural networks

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Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works

Explaining and Harnessing Adversarial Examples

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381 · see citing works

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

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Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143 · see citing works

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Nguyen, A., Yosinski, J., & Clune, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298640 · Cited by 3,258 · see citing works

MatConvNet: Convolutional Neural Networks for MATLAB

Vedaldi, A., & Lenc, K. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2807412 · Cited by 2,444 · see citing works

Convolutional networks and applications in vision

LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010) · Proceedings of 2010 IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.2010.5537907 · Cited by 2,213 · see citing works

Object Recognition with Gradient-Based Learning

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Nonlinear Optimization

Ruszczyński, A. (2006) · Princeton University Press eBooks · doi:10.1515/9781400841059 · Cited by 703 · see citing works

Towards Deep Neural Network Architectures Robust to Adversarial ExamplesPreprint

Gu, S., & Rigazio, L. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.5068 · Cited by 658 · see citing works

Strategies for training large scale neural network language models

Mikolov, T., Deoras, A., Povey, D., Burget, L., & Černocký, J. (2011) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2011.6163930 · Cited by 516 · see citing works

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

Spencer, M., Eickholt, J., & Cheng, J. J. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2343960 · Cited by 297 · see citing works

Analysis of classifiers’ robustness to adversarial perturbations

Fawzi, A., Fawzi, O., & Pascal Frossard, B. (2017) · Machine Learning · doi:10.1007/s10994-017-5663-3 · Cited by 289 · see citing works

Deep autoencoder neural networks for gene ontology annotation predictions

Chicco, D., Sadowski, P., & Baldi, P. (2014) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2649387.2649442 · Cited by 185 · see citing works

Manitest: Are classifiers really invariant?

Fawzi, A., & Pascal Frossard, B. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.106 · Cited by 89 · see citing works

Least-Change Secant Update Methods for Underdetermined Systems

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What Is Holding Back Convnets for Detection?

Pepik, B., Benenson, R., Ritschel, T., & Schiele, B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24947-6_43 · Cited by 45 · see citing works

Towards Deep Learning Models Resistant to Adversarial Attacks

Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017) · arXiv · doi:10.48550/arxiv.1706.06083 · Cited by 6,602 · see citing works

Explainable AI: a review of machine learning interpretability methods

Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020) · Entropy · doi:10.3390/e23010018 · Cited by 2,922 · see citing works

Universal Adversarial Perturbations

Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., & Frossard, P. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.17 · Cited by 2,680 · see citing works

Adversarial Examples: Attacks and Defenses for Deep Learning

Yuan, X., He, P., Zhu, Q., & Li, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2886017 · Cited by 1,733 · see citing works

Explaining nonlinear classification decisions with deep taylor decomposition

Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.008 · Cited by 1,357 · see citing works

Machine Learning Testing: Survey, Landscapes and Horizons

Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2962027 · Cited by 897 · see citing works

Interpretation of Neural Networks Is Fragile

Ghorbani, A., Abid, A., & Zou, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013681 · Cited by 765 · see citing works

HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

Chen, J., Jordan, M. I., & Wainwright, M. J. (2020) · 2020 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp40000.2020.00045 · Cited by 718 · see citing works

Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations

Ganju, K., Wang, Q., Yang, W., Gunter, C. A., & Borisov, N. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243834 · Cited by 475 · see citing works

Attack of the tails: Yes, you really can backdoor federated learning

Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-Y., Lee, K., & Papailiopoulos, D. (2020) · arXiv · doi:10.48550/arxiv.2007.05084 · Cited by 406 · see citing works

Generative Adversarial Networks: A Survey Toward Private and Secure Applications

Cai, Z., Xiong, Z., Xu, H., Wang, P., Li, W., & Pan, Y. (2022) · ACM Computing Surveys · doi:10.1145/3459992 · Cited by 376 · see citing works

Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments

Bai, X., Wang, X., Liu, X., Liu, Q., Song, J., Sebe, N., & Kim, B. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108102 · Cited by 313 · see citing works

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Zhang, K., Wen, Q., Zhang, C., Cai, R., Jin, M., Liu, Y., Zhang, J. Y., Liang, Y., Pang, G., Song, D., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3387317 · Cited by 295 · see citing works

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2022) · ACM Computing Surveys · doi:10.1145/3453158 · Cited by 295 · see citing works

Federated learning in a medical context: A systematic literature review

Pfitzner, B., Steckhan, N., & Arnrich, B. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3412357 · Cited by 288 · see citing works

Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree

Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265 · see citing works

The neuroconnectionist research programme

Doerig, A., Sommers, R. P., Seeliger, K., Richards, B., Ismael, J., Lindsay, G. W., Kording, K. P., Konkle, T., van Gerven, M. A. J., Kriegeskorte, N., et al. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00705-w · Cited by 233 · see citing works

Causal interpretability for machine learning-problems, methods and evaluation

Moraffah, R., Karami, M., Guo, R., Raglin, A., & Liu, H. (2020) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3400051.3400058 · Cited by 238 · see citing works

Disentangling Adversarial Robustness and Generalization

Stutz, D., Hein, M., & Schiele, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00714 · Cited by 223 · see citing works

Vision Transformers Are Robust Learners

Paul, S., & Chen, P.-Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20103 · Cited by 213 · see citing works

Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., & Wang, Z. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00078 · Cited by 205 · see citing works

Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ML

Rasool, R. U., Ahmad, H. F., Rafique, W., Qayyum, A., & Qadir, J. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103332 · Cited by 177 · see citing works

Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations

Mandlekar, A., Zhu, Y., Garg, A., Fei-Fei, L., & Savarese, S. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206245 · Cited by 116 · see citing works

Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks

Bradshaw, J., Matthews, A. G. D. G., & Ghahramani, Z. (2017) · arXiv · doi:10.48550/arxiv.1707.02476 · Cited by 109 · see citing works

Free lunch for testing: fuzzing deep-learning libraries from open source

Wei, A., Deng, Y., Yang, C., & Zhang, L. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510041 · Cited by 106 · see citing works

Adversarial attacks on an oblivious recommender

Christakopoulou, K., & Banerjee, A. (2019) · Proceedings of the 13th ACM Conference on Recommender Systems · doi:10.1145/3298689.3347031 · Cited by 105 · see citing works

Privacy and Security of Big Data in AI Systems: A Research and Standards Perspective

Dilmaghani, S., Brust, M. R., Danoy, G., Cassagnes, N., Pecero, J., & Bouvry, P. (2019) · 2019 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata47090.2019.9006283 · Cited by 111 · see citing works

Universal Adversarial Perturbation via Prior Driven Uncertainty Approximation

Liu, H., Ji, R., Li, J., Zhang, B., Gao, Y., Wu, Y., & Huang, F. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00303 · Cited by 95 · see citing works

Fuzzing deep-learning libraries via automated relational API inference

Deng, Y., Yang, C., Wei, A., & Zhang, L. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549085 · Cited by 86 · see citing works

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Kuutti, S., Bowden, R., Jin, Y., Barber, P., & Fallah, S. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2962338 · Cited by 96 · see citing works

Boosting Fast Adversarial Training With Learnable Adversarial Initialization

Jia, X., Zhang, Y., Wu, B., Wang, J., & Cao, X. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3184255 · Cited by 69 · see citing works

Grey-box Adversarial Attack And Defence For Sentiment Classification

Xu, Y., Zhong, X., Jimeno Yepes, A., & Lau, J. H. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.321 · Cited by 54 · see citing works

On failures of RGB cameras and their effects in autonomous driving applications

Secci, F., & Ceccarelli, A. (2020) · 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre5003.2020.00011 · Cited by 42 · see citing works

Text Backdoor Detection Using an Interpretable RNN Abstract Model

Fan, M., Si, Z., Xie, X., Liu, Y., & Liu, T. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3103064 · Cited by 38 · see citing works

Extendable and explainable deep learning for pan-cancer radiogenomics research

Liu, Q., & Hu, P. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.102111 · Cited by 27 · see citing works

Dispersed Pixel Perturbation-Based Imperceptible Backdoor Trigger for Image Classifier Models

Wang, Y., Zhao, M., Li, S., Yuan, X., & Ni, W. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3202687 · Cited by 26 · see citing works

A regularization method to improve adversarial robustness of neural networks for ECG signal classification

Ma, L., & Liang, L. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105345 · Cited by 26 · see citing works

Robust Vision-Based Cheat Detection in Competitive Gaming

Jonnalagadda, A., Frosio, I., Schneider, S., McGuire, M., & Kim, J. (2021) · Proceedings of the ACM on Computer Graphics and Interactive Techniques · doi:10.1145/3451259 · Cited by 23 · see citing works

Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-Modal Fake News Detection

Chen, J., Jia, C., Zheng, H., Chen, R., & Fu, C. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3249290 · Cited by 17 · see citing works

Human in the AI Loop via xAI and Active Learning for Visual Inspection

Rožanec, J. M., Montini, E., Cutrona, V., Papamartzivanos, D., Klemenčič, T., Fortuna, B., Mladenić, D., Veliou, E., Giannetsos, T., & Emmanouilidis, C. (2024) · Artificial Intelligence in Manufacturing · doi:10.1007/978-3-031-46452-2_22 · Cited by 17 · see citing works

Estimating Labeling Quality with Deep Object Detectors

Haase-Schütz, C., Hertlein, H., & Wiesbeck, W. (2019) · 2019 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2019.8814144 · Cited by 8 · see citing works

Detecting Scene-Plausible Perceptible Backdoors in Trained DNNs Without Access to the Training Set

Xiang, Z., Miller, D. J., Wang, H., & Kesidis, G. (2021) · Neural Computation · doi:10.1162/neco_a_01376 · Cited by 8 · see citing works

UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images

Qu, Y., Shen, X., Wu, Y., Backes, M., Zannettou, S., & Zhang, Y. (2025) · Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3719027.3765088 · Cited by 6 · see citing works

ИСПОЛЬЗОВAНИЕ ПОТЕНЦИAЛA СОЦИAЛЬНЫХ ПAРТНЕРОВ В ПОДГОТОВКЕ БУДУЩИХ ПЕДAГОГОВ

Атабекова, Б. Б. (2024) · Habaršy. Pedagogika ġylymdary seriâsy · doi:10.51889/2959-5762.2023.80.4.019

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

New Characterizations of Simple Points, Minimal Non-simple Sets and P-Simple Points in 2D, 3D and 4D Discrete Spaces

Couprie, M., & Bertrand, G. (2008) · Springer eBooks · doi:10.1007/978-3-540-79126-3_11 · Cited by 9 · see citing works

The [0-Simple] Simple Subsemigroups of Nonnegative Matrices

Shuang-ping, Q. (2000) · Journal of Mathematical Research and Exposition