Universal Adversarial Perturbations

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

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

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · 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

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

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542 · see citing works

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

Moosavi-Dezfooli, S.-M., Fawzi, A., & Frossard, P. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.282 · Cited by 5,317 · see citing works

Return of the Devil in the Details: Delving Deep into Convolutional Nets

Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335 · 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

Evasion attacks against machine learning at test time

Biggio, B., Corona, I., Maiorca, D., Nelson, B. A., Srndic, N., Laskov, P., Giacinto, G., & Roli, F. (2013) · Cited by 1,622 · see citing works

Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis

Le, Q. V., Zou, W. Y., Yeung, S. Y., & Ng, A. Y. (2011) · 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/cvpr.2011.5995496 · Cited by 966 · see citing works

Evasion Attacks against Machine Learning at Test Time

Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., & Roli, F. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40994-3_25 · Cited by 890 · see citing works

Learning with a Strong AdversaryPreprint

Huang, R., Xu, B., Schuurmans, D., & Szepesvari, C. (2015) · arXiv.org · doi:10.48550/arxiv.1511.03034 · Cited by 381 · 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

Adversarial Diversity and Hard Positive Generation

Rozsa, A., Rudd, E. M., & Boult, T. E. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.58 · Cited by 268 · see citing works

Measuring Neural Net Robustness with ConstraintsPreprint

Bastani, O., Ioannou, Y., Lampropoulos, L., Vytiniotis, D., Nori, A., & Criminisi, A. (2016) · arXiv (Cornell University) · doi:10.17863/cam.26484 · Cited by 257 · see citing works

Robustness of classifiers: from adversarial to random noisePreprint

Fawzi, A., Moosavi-Dezfooli, S.-M., & Pascal Frossard, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.08967 · Cited by 238 · see citing works

Exploring the space of adversarial images

Tabacof, P., & Junior, E. A. D. V. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727230 · Cited by 176 · see citing works

Adversarial Manipulation of Deep RepresentationsPreprint

Sabour, S., Cao, Y., Faghri, F., & Fleet, D. J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05122 · Cited by 164 · see citing works

Boosting Adversarial Attacks with Momentum

Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., & Li, J. (2018) · 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/cvpr.2018.00957 · Cited by 3,168 · 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

Explaining Explanations: An Overview of Interpretability of Machine Learning

Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00018 · Cited by 2,507 · see citing works

One Pixel Attack for Fooling Deep Neural Networks

Su, J., Vargas, D. V., & Sakurai, K. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2890858 · Cited by 2,406 · see citing works

Robust physical-world attacks on deep learning visual classification

Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., & Song, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00175 · Cited by 2,173 · see citing works

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Akhtar, N., & Mian, A. (2018) · IEEE Access · doi:10.1109/access.2018.2807385 · Cited by 2,021 · see citing works

Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

Xu, W., Evans, D., & Qi, Y. (2018) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23198 · Cited by 1,859 · see citing works

ZOO

Chen, P., Zhang, H., Sharma, Y., Yi, J., & Hsieh, C. (2017) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3128572.3140448 · Cited by 1,782 · 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

Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks

Wang, B., Yao, Y., Shan, S., Li, H., Viswanath, B., Zheng, H.-T., & Zhao, B. Y. (2019) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2019.00031 · Cited by 1,486 · see citing works

Adversarial Examples for Evaluating Reading Comprehension Systems

Jia, R., & Liang, P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1215 · Cited by 1,410 · see citing works

BadNets: Evaluating Backdooring Attacks on Deep Neural Networks

Gu, T., Liu, K., Dolan-Gavitt, B., & Garg, S. (2019) · IEEE Access · doi:10.1109/access.2019.2909068 · Cited by 1,238 · see citing works

Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

Carlini, N., & Wagner, D. (2018) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2018.00009 · Cited by 1,071 · see citing works

Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Chen, X., Liu, C., Li, B., Lu, K., & Song, D. (2017) · arXiv · doi:10.48550/arxiv.1712.05526 · Cited by 1,039 · see citing works

Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks

Dong, Y., Pang, T., Su, H., & Zhu, J. (2019) · 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/cvpr.2019.00444 · Cited by 1,032 · see citing works

Countering Adversarial Images using Input TransformationsPreprint

Guo, C., Rana, M., Cissé, M., & van der Maaten, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.00117 · Cited by 1,151 · see citing works

Synthesizing Robust Adversarial ExamplesPreprint

Athalye, A., Engstrom, L., Ilyas, A., & Kwok, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.07397 · Cited by 973 · see citing works

Enhancing The Reliability of Out-of-distribution Image Detection in Neural NetworksPreprint

Liang, S., Li, Y., & Srikant, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02690 · Cited by 1,117 · see citing works

Adversarial Examples for Semantic Segmentation and Object Detection

Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., & Yuille, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.153 · Cited by 955 · see citing works

Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

Jin, D., Jin, Z., Zhou, J. T., & Szolovits, P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6311 · Cited by 1,108 · see citing works

Proceedings of the 29th ACM International Conference on Multimedia

Yue, J., Li, H., Wei, P., Li, G., & Lin, L. (2021) · doi:10.1145/3474085 · Cited by 927 · see citing works

A Survey on Neural Network Interpretability

Zhang, Y., Tino, P., Leonardis, A., & Tang, K. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3100641 · Cited by 923 · see citing works

Explainable Artificial Intelligence in education

Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100074 · Cited by 863 · see citing works

Black-box Adversarial Attacks with Limited Queries and Information

Ilyas, A., Engstrom, L., Athalye, A., & Lin, J. (2018) · arXiv · doi:10.48550/arxiv.1804.08598 · Cited by 806 · see citing works

On instabilities of deep learning in image reconstruction and the potential costs of AI

Antun, V., Renna, F., Poon, C., Adcock, B., & Hansen, A. C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907377117 · Cited by 792 · see citing works

Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis

Karimi, D., Dou, H., Warfield, S. K., & Gholipour, A. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101759 · Cited by 725 · see citing works

Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection

Thys, S., Ranst, W. V., & Goedemé, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00012 · Cited by 770 · see citing works

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Raschka, S., Patterson, J. M., & Nolet, C. (2020) · Information · doi:10.3390/info11040193 · Cited by 777 · see citing works

Adversarial Attacks and Defenses in Images, Graphs and Text: A Review

Xu, H., Ma, Y., Liu, H., Deb, D., Liu, H., Tang, J., & Jain, A. K. (2020) · Machine Intelligence Research · doi:10.1007/s11633-019-1211-x · Cited by 716 · see citing works

Recent Progress on Generative Adversarial Networks (GANs): A Survey

Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., & Zheng, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2905015 · Cited by 699 · see citing works

Backdoor Learning: A Survey

Li, Y., Jiang, Y., Li, Z., & Xia, S.-T. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3182979 · Cited by 682 · see citing works

Adversarial Attacks and Defenses in Deep Learning

Ren, K., Zheng, T., Qin, Z., & Liu, X. (2020) · Engineering · doi:10.1016/j.eng.2019.12.012 · Cited by 678 · see citing works

Universal Adversarial Triggers for Attacking and Analyzing NLP

Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1221 · Cited by 642 · see citing works

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Chen, P., Sharma, Y., Zhang, H., Yi, J., & Hsieh, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11302 · Cited by 594 · see citing works

Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization

Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., & Roli, F. (2017) · Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3128572.3140451 · Cited by 549 · see citing works

Knockoff Nets: Stealing Functionality of Black-Box Models

Orekondy, T., Schiele, B., & Fritz, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00509 · Cited by 549 · see citing works

A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability

Huang, X., Kroening, D., Ruan, W., Sharp, J. J., Sun, Y., Thamo, E., Wu, M., & Yi, X. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100270 · Cited by 567 · see citing works

Adversarially Robust Generalization Requires More DataPreprint

Schmidt, L., Santurkar, S., Tsipras, D., Talwar, K., & Ma̧dry, A. (2018) · Neural Information Processing Systems · doi:10.48550/arxiv.1804.11285 · Cited by 891 · see citing works

Deep Learning in Radiology

McBee, M. P., Awan, O. A. R., Colucci, A. T., Ghobadi, C. W., Kadom, N., Kansagra, A. P., Tridandapani, S., & Auffermann, W. F. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.02.018 · Cited by 502 · see citing works

Generating Natural Adversarial Examples

Zhao, Z., Dua, D., & Singh, S. (2017) · arXiv · doi:10.48550/arxiv.1710.11342 · Cited by 508 · 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

Deep Neural Networks Motivated by Partial Differential Equations

Ruthotto, L., & Haber, E. (2019) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-019-00903-1 · Cited by 478 · see citing works

Interpretability of deep learning models: A survey of results

Chakraborty, S., Tomsett, R., Raghavendra, R., Harborne, D., Alzantot, M., Cerutti, F., Srivastava, M. B., Preece, A. D., Julier, S., Rao, R., et al. (2017) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/uic-atc.2017.8397411 · Cited by 469 · see citing works

When Machine Learning Meets Privacy: A Survey and Outlook

Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F., & Lin, Z. (2022) · ACM Computing Surveys · doi:10.1145/3436755 · Cited by 455 · see citing works

Explainable Deep Learning: A Field Guide for the Uninitiated

Ras, G., Xie, N., van Gerven, M. A. J., & Doran, D. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13200 · Cited by 486 · see citing works

Semantically Equivalent Adversarial Rules for Debugging NLP models

Ribeiro, M. T., Singh, S., & Guestrin, C. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1079 · Cited by 435 · see citing works

Deep learning in ECG diagnosis: A review

Liu, X., Wang, H., Li, Z., & Qin, L. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107187 · Cited by 443 · see citing works

Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition

Dong, Y., Su, H., Wu, B., Li, Z., Liu, W., Zhang, T., & Zhu, J. (2019) · 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/cvpr.2019.00790 · Cited by 433 · see citing works

A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View

Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. M. (2018) · IEEE Access · doi:10.1109/access.2018.2805680 · Cited by 421 · see citing works

ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation

Liu, Y., Lee, W., Tao, G., Ma, S., Aafer, Y., & Zhang, X. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363216 · Cited by 419 · see citing works

Review of Artificial Intelligence Adversarial Attack and Defense Technologies

Qiu, S., Liu, Q., Zhou, S., & Wu, C. (2019) · Applied Sciences · doi:10.3390/app9050909 · Cited by 412 · see citing works

A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions

Dodge, S., & Karam, L. J. (2017) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2017.8038465 · Cited by 408 · see citing works

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

Bouwmans, T., Javed, S., Sultana, M., & Jung, S. K. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.024 · Cited by 401 · see citing works

SafetyNet: Detecting and Rejecting Adversarial Examples Robustly

Lu, J., Issaranon, T., & Forsyth, D. A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.56 · Cited by 397 · see citing works

Adversarial Personalized Ranking for Recommendation

He, X., He, Z., Du, X., & Chua, T. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3209981 · Cited by 386 · see citing works

Understanding adversarial training: Increasing local stability of supervised models through robust optimization

Shaham, U., Yamada, Y., & Negahban, S. N. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.04.027 · Cited by 355 · see citing works

Weight Poisoning Attacks on Pretrained Models

Kurita, K., Michel, P., & Neubig, G. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.249 · Cited by 354 · see citing works

Adversarial Attacks on Deep-Learning Based Radio Signal Classification

Sadeghi, M., & Larsson, E. G. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2018.2867459 · Cited by 349 · see citing works

Adversarial frontier stitching for remote neural network watermarking

Merrer, E. L., Pérez, P., & Trédan, G. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04434-z · Cited by 342 · see citing works

Cyber security meets artificial intelligence: a survey

Li, J. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1800573 · Cited by 331 · see citing works

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3162397 · Cited by 362 · see citing works

Application of deep learning to cybersecurity: A survey

Mahdavifar, S., & Ghorbani, A. A. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.056 · Cited by 329 · see citing works

On the Robustness of Semantic Segmentation Models to Adversarial Attacks

Arnab, A., Mikšík, O., & Torr, P. H. S. (2018) · 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/cvpr.2018.00099 · Cited by 323 · see citing works

MemGuard

Jia, J., Salem, A., Michael Backes, A., Zhang, Y., & Gong, N. Z. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363201 · Cited by 374 · see citing works

Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization

Li, S., Xue, M., Zhao, B. Z. H., Zhu, H., & Zhang, X. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2020.3021407 · Cited by 325 · 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

Advances in Adversarial Attacks and Defenses in Computer Vision: A Survey

Akhtar, N., Mian, A. S., Kardan, N., & Shah, M. (2021) · IEEE Access · doi:10.1109/access.2021.3127960 · Cited by 319 · see citing works

A Backdoor Attack Against LSTM-Based Text Classification Systems

Dai, J., Chen, C., & Li, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2941376 · Cited by 298 · 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

Advhat: Real-world adversarial attack on arcface face id system

Komkov, S., & Petiushko, A. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9412236 · Cited by 294 · see citing works

Universal Adversarial Perturbations Against Semantic Image Segmentation

Metzen, J. H., Kumar, M. C., Brox, T., & Fischer, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.300 · Cited by 287 · see citing works

Towards Reverse-Engineering Black-Box Neural Networks

Oh, S. J., Schiele, B., & Fritz, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-28954-6_7 · Cited by 291 · see citing works

Adversarial Attacks in Modulation Recognition With Convolutional Neural Networks

Lin, Y., Zhao, H., Ma, X., Tu, Y., & Wang, M. (2020) · IEEE Transactions on Reliability · doi:10.1109/tr.2020.3032744 · Cited by 291 · see citing works

Robust Semantic Communications With Masked VQ-VAE Enabled Codebook

Hu, Q., Zhang, G., Qin, Z., Cai, Y., Yu, G., & Li, G. Y. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3265201 · Cited by 289 · see citing works

Privacy and Security Issues in Deep Learning: A Survey

Liu, X., Xie, L., Wang, Y., Zou, J., Xiong, J., Ying, Z., & Vasilakos, A. V. (2020) · IEEE Access · doi:10.1109/access.2020.3045078 · Cited by 285 · see citing works

All You Need Is Low (Rank)

Entezari, N., Al-Sayouri, S. A., Darvishzadeh, A., & Papalexakis, E. E. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371789 · Cited by 283 · see citing works

Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding

Schönherr, L., Kohls, K. S., Zeiler, S., Holz, T., & Kolossa, D. (2019) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2019.23288 · Cited by 270 · see citing works

Deep Learning: The Good, the Bad, and the Ugly

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Clean-Label Backdoor Attacks on Video Recognition Models

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Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG CompressionPreprint

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Adversarial Examples: Opportunities and Challenges

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Practical Black-Box Attacks on Deep Neural Networks Using Efficient Query Mechanisms

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Adversarial Learning Targeting Deep Neural Network Classification: A Comprehensive Review of Defenses Against Attacks

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Learning to Attack: Adversarial Transformation Networks

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Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure

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Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming

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A comprehensive overview of biometric fusion

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Backdoor Attacks Against Deep Learning Systems in the Physical World

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Generalizable Data-Free Objective for Crafting Universal Adversarial Perturbations

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Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

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SparseFool: A Few Pixels Make a Big Difference

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A Method for Computing Class-wise Universal Adversarial PerturbationsPreprint

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

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Universal Adversarial Perturbations: A SurveyPreprint

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