Robustness of Deep Learning Models for Vision Tasks
Source abstract
Abstract
In recent years, artificial intelligence technologies in vision tasks have gradually begun to be applied to the physical world, proving they are vulnerable to adversarial attacks. Thus, the importance of improving robustness against adversarial attacks has emerged as an urgent issue in vision tasks. This article aims to provide a historical summary of the evolution of adversarial attacks and defense methods on CNN-based models and also introduces studies focusing on brain-inspired models that mimic the visual cortex, which is resistant to adversarial attacks. As the origination of CNN models was in the application of physiological findings related to the visual cortex of the time, new physiological studies related to the visual cortex provide an opportunity to create more robust models against adversarial attacks. The authors hope this review will promote interest and progress in artificially intelligent security by improving the robustness of deep learning models for vision tasks.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Adversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Bacillus and Francisella bacterial research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electronics, Chungwoon University, Incheon 22100, Republic of Korea
Department of AIOT, Sangmyung University, Seoul 03016, Republic of Korea
Funding
National Research Foundation of Korea
References (100+)
Show source-provided references
1. Hinton, G., and Neural, R.S.-A. (2007, January 7). Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes. Proceedings of the 20th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada.
2. Ahmed, A., Yu, K., Xu, W., Gong, Y., and Xing, E. (2008). Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks, Springer.10.1007/978-3-540-88690-7_6 ↗
3. Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2006, January 4–7). Greedy Layer-Wise Training of Deep Networks. Proceedings of the 19th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada.10.7551/mitpress/7503.003.0024 ↗
4. An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation
5. Lee, H., Grosse, R., Ranganath, R., and Ng, A.Y. (2009, January 14–18). Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, QC, Canada.10.1145/1553374.1553453 ↗
6. Ranzato, M., Boureau, Y.L., and Cun, Y. (2007). Advances in Neural Information Processing Systems, MIT Press.
7. Aurelio, M., Poultney, R.C., Chopra, S., and Lecun, Y. (2006). Advances in Neural Information Processing Systems, MIT Press.
8. Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.A. (2008, January 11–15). Extracting and Composing Robust Features with Denoising Autoencoders. Proceedings of the 25th International Conference on Machine Learning, Montreal, QC, Canada.10.1145/1390156.1390294 ↗
9. Reducing the Dimensionality of Data with Neural Networks10.1126/science.1127647 ↗
10. Salakhutdinov, R., and Hinton, G. (2007, January 21–24). Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, San Juan, Puerto Rico.
11. Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style
12. Taylor, G., Hinton, G.E., and Roweis, S. (2006). Advances in Neural Information Processing Systems, MIT Press.
13. Osindero, S., and Hinton, G.E. (2007). Advances in Neural Information Processing Systems, MIT Press.
14. Ranzato, M., and Szummer, M. (2008, January 5–9). Semi-Supervised Learning of Compact Document Representations with Deep Networks. Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland.10.1145/1390156.1390256 ↗
15. Salakhutdinov, R., and Approximate, G.H.-I.J. (2009). Semantic Hashing, Elsevier.10.1016/j.ijar.2008.11.006 ↗
16. Many-Layered Learning10.1162/08997660260293319 ↗
17. Hadsell, R., Erkan, A., Sermanet, P., Scoffier, M., Muller, U., and LeCun, Y. (2008, January 22–26). Deep Belief Net Learning in a Long-Range Vision System for Autonomous off-Road Driving. Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France.10.1109/IROS.2008.4651217 ↗
18. Xie, D., and Bai, L. (2015, January 9–11). A Hierarchical Deep Neural Network for Fault Diagnosis on Tennessee-Eastman Process. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.10.1109/ICMLA.2015.208 ↗
19. Zhang, L., Yang, F., Zhang, Y.D., and Zhu, Y.J.I. (2016, January 25–28). Road Crack Detection Using Deep Convolutional Neural Network. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.10.1109/ICIP.2016.7533052 ↗
20. Deep Learning in Medical Imaging: General Overview10.3348/kjr.2017.18.4.570 ↗
21. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2013). Intriguing Properties of Neural Networks. arXiv.
22. Drenkow, N., Sani, N., Shpitser, I., and Unberath, M. (2021). A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the Gap?. arXiv.
23. Yan, H., and Tan, V.Y.F. (2020). Towards Adversarial Robustness of Deep Vision Algorithms. arXiv.
24. Towards Evaluating the Robustness of Adversarial Attacks Against Image Scaling Transformation10.23919/cje.2021.00.309 ↗
25. Robustness Verification for Machine-Learning-Based Power System Dynamic Security Assessment Models Under Adversarial Examples10.1109/TCNS.2022.3145285 ↗
26. Ibrahim, M.S., Dong, W., and Yang, Q. (2020). Machine learning driven smart elctric power systems: Current trens and new perspectives. Appl. Energy, 272.10.1016/j.apenergy.2020.115237 ↗
27. Chattopadhyay, N., Chatterjee, S., and Chattopadhyay, A. (2022). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.
28. Re-Thinking Model Robustness from Stability: A New Insight to Defend Adversarial Examples10.1007/s10994-022-06186-9 ↗
29. Borji, A., Ai, Q., and Francisco, S. (2002). Overparametrization Improves Robustness against Adversarial Attacks: A Replication Study. arXiv.
30. Borji, A., Ai, Q., and Francisco, S. (2022). Is Current Research on Adversarial Robustness Addressing the Right Problem?. arXiv.
31. Wang, Y., Tan, Y.A., Baker, T., Kumar, N., and Zhang, Q. (2022). IEEE Transactions on Industrial Informatics, IEEE.
32. Jankovic, A., and Mayer, R. (2022, January 27). An Empirical Evaluation of Adversarial Examples Defences, Combinations and Robustness Scores. Proceedings of the IWSPA ’22: Proceedings of the 2022 ACM on International Workshop on Security and Privacy Analytics, Baltimore, MD, USA.10.1145/3510548.3519370 ↗
33. Poggio, T. (1981). Marr’s Computational Approach to Vision, Elsevier.10.1016/0166-2236(81)90081-3 ↗
34. Ungerleider, L., and Haxby, J.V. (1994). ’What’ and ’Where’ in the Human Brain, Elsevier.10.1016/0959-4388(94)90066-3 ↗
35. Markov, N.T., Ercsey-Ravasz, M., van Essen, D.C., Knoblauch, K., Toroczkai, Z., and Kennedy, H. (2013). Cortical High-Density Counterstream Architectures. Science, 342.10.1126/science.1238406 ↗
36. Fattori, P., Pitzalis, S., and Galletti, C. (2009). The Cortical Visual Area V6 in Macaque and Human Brains, Elsevier.10.1016/j.jphysparis.2009.05.012 ↗
37. Concurrent Processing Streams in Monkey Visual Cortex10.1016/0166-2236(88)90130-0 ↗
38. Reflections on the Past Two Decades of Neuroscience10.1038/s41583-020-0363-6 ↗
39. A Model of V4 Shape Selectivity and Invariance10.1152/jn.01265.2006 ↗
40. Robust Object Recognition with Cortex-like Mechanisms10.1109/TPAMI.2007.56 ↗
41. A Fast Learning Algorithm for Deep Belief Nets10.1162/neco.2006.18.7.1527 ↗
42. Neural Networks and Neuroscience-Inspired Computer Vision10.1016/j.cub.2014.08.026 ↗
43. Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.
44. Cai, Z., and Vasconcelos, N. (2018, January 18–23). Cascade R-Cnn: Delving into High Quality Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00644 ↗
45. Shreyas, E., and Sheth, M.H. (2021, January 27–28). 3D Object Detection and Tracking Methods Using Deep Learning for Computer Vision Applications. Proceedings of the 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India.10.1109/RTEICT52294.2021.9573964 ↗
46. Deep Learning—Based Motion Tracking Using Ultrasound Images
47. Multi-Layered Deep Learning Features Fusion for Human Action Recognition
48. ISPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition10.1109/ACCESS.2021.3078184 ↗
49. Liu, Z., Chen, H., Feng, R., Wu, S., Ji, S., Yang, B., and Wang, X. (2021, January 20–25). Deep Dual Consecutive Network for Human Pose Estimation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.00059 ↗
50. Wang, J., Jin, S., Liu, W., Liu, W., Qian, C., and Luo, P. (2021, January 20–25). When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.01168 ↗
51. Gradient-Based Learning Applied to Document Recognition10.1109/5.726791 ↗
52. ImageNet Classification with Deep Convolutional Neural Networks10.1145/3065386 ↗
53. Carreira-Perpinan, M., and Hinton, G. (2016, January 24). On Contrastive Divergence Learning. Proceedings of the Machine Learning Research, New York, NY, USA.
54. Hinton, G.E. (2012). Lecture Notes in Computer Science (Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.
55. Nair, V., and Hinton, G.E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines, Department of Computer Science, University of Toronto.
56. Zeiler, M.D., and Fergus, R. (2013). Stochastic Pooling for Regularization of Deep Convolutional Neural Networks. arXiv.
57. Liou, C., Cheng, W., Liou, J., and Liou, D.R. (2014). Autoencoder for Words, Elsevier.10.1016/j.neucom.2013.09.055 ↗
58. Emergence of Phase-and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces10.1162/089976600300015312 ↗
59. Olshausen, B., and Field, D.J. (1997). Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1?, Elsevier.10.1016/S0042-6989(97)00169-7 ↗
60. Yu, K., Zhang, T., and Gong, Y. (2009, January 7–10). Nonlinear Learning Using Local Coordinate Coding. Proceedings of the 23rd Annual Conference on Neural Information Processing Systems 2009, Vancouver, BC, Canada.
61. Self-Taught Learning: Transfer Learning from Unlabeled Data
62. Wang, J., Yang, J., Yu, K., Huang, T., and Gong, Y. (2010, January 13–18). Locality-Constrained Linear Coding for Image Classification. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.10.1109/CVPR.2010.5540018 ↗
63. DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks10.1109/TPAMI.2016.2587642 ↗
64. Colitis Detection on Abdominal CT Scans by Rich Feature Hierarchies
65. Luo, G., An, R., Wang, K., and Zhang, H. (2016, January 11–14). A Deep Learning Network for Right Ventricle Segmentation in Short-Axis MRI. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.10.22489/CinC.2016.139-406 ↗
66. Efficient Saliency-Based Object Detection in Remote Sensing Images Using Deep Belief Networks10.1109/LGRS.2015.2498644 ↗
67. Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data10.1109/TPAMI.2012.277 ↗
68. Doulamis, N., and Doulamis, A. (2012). Lecture Notes in Computer Science (Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.
69. Face Recognition: A Convolutional Neural-Network Approach10.1109/72.554195 ↗
70. Openface: A General-Purpose Face Recognition Library with Mobile Applications
71. A Top-down Event-Driven Approach for Concurrent Activity Recognition10.1007/s11042-012-0993-4 ↗
72. Improving Multi-Camera Activity Recognition by Employing Neural Network Based Readjustment10.1080/08839514.2012.629540 ↗
73. Makantasis, K., Doulamis, A., Doulamis, N., and Psychas, K. (2016, January 25–28). Deep Learning Based Human Behavior Recognition in Industrial Workflows. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.10.1109/ICIP.2016.7532630 ↗
74. Gan, C., Wang, N., Yang, Y., Yeung, D.-Y., and Hauptmann, A.G. (2015, January 7–12). Devnet: A Deep Event Network for Multimedia Event Detection and Evidence Recounting. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.10.1109/CVPR.2015.7298872 ↗
75. Activity Recognition in Beach Volleyball Using a Deep Convolutional Neural Network: Leveraging the Potential of Deep Learning in Sports10.1007/s10618-017-0495-0 ↗
76. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei, L. (2014, January 23–28). Large-Scale Video Classification with Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.10.1109/CVPR.2014.223 ↗
77. Human Activity Recognition with Smartphone Sensors Using Deep Learning Neural Networks10.1016/j.eswa.2016.04.032 ↗
78. Crowded Scene Understanding by Deeply Learned Volumetric Slices10.1109/TCSVT.2016.2593647 ↗
79. Tang, K., Yao, B., Fei-Fei, L., and Koller, D. (2013, January 1–8). Combining the Right Features for Complex Event Recognition. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia.10.1109/ICCV.2013.335 ↗
80. Song, S., Chandrasekhar, V., Mandal, B., Li, L., Lim, J.-H., Sateesh Babu, G., Phyo San, P., and Cheung, N.-M. (July, January 26). Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USA.10.1109/CVPRW.2016.54 ↗
81. Kavi, R., Kulathumani, V., and Kecojevic, V. (2016). Multiview Fusion for Activity Recognition Using Deep Neural Networks. J. Electron. Imaging, 25.10.1117/1.JEI.25.4.043010 ↗
82. Kitsikidis, A., Dimitropoulos, K., Douka, S., and Grammalidis, N. (2014). Dance Analysis Using Multiple Kinect Sensors, SciTePress.
83. Pictorial Structures for Object Recognition10.1023/B:VISI.0000042934.15159.49 ↗
84. Jain, A., Tompson, J., Andriluka, M., Taylor, G.W., and Bregler, C. (2013). Learning Human Pose Estimation Features with Convolutional Networks. arXiv.
85. Receptive Fields of Single Neurones in the Cat’s Striate Cortex10.1113/jphysiol.1959.sp006308 ↗
86. Fukushima, K. (1988). Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition, Elsevier.10.1016/0893-6080(88)90014-7 ↗
87. Deep Learning
88. A Survey of the Recent Architectures of Deep Convolutional Neural Networks10.1007/s10462-020-09825-6 ↗
89. Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H. (2021). CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope. Electronics, 10.10.3390/electronics10202470 ↗
90. Zeiler, M., and Vision, R.F.-E. (2014). Visualizing and Understanding Convolutional Networks, Springer.10.1007/978-3-319-10590-1_53 ↗
91. Zeiler, M., Taylor, G., and Fergus, R. (2011, January 6–13). Adaptive Deconvolutional Networks for Mid and High Level Feature Learning. Proceedings of the 2011 IEEE International Conference on Computer Vision, Barcelona, Spain.10.1109/ICCV.2011.6126474 ↗
92. Simonyan, K., and Zisserman, A. (2015, January 7–9). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015—Conference Track Proceedings, San Diego, CA, USA.
93. ImageNet Large Scale Visual Recognition Challenge10.1007/s11263-015-0816-y ↗
94. Variations of AlexNet and GoogLeNet to Improve Korean Character Recognition Performance
95. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
96. Srivastava, R.K., Greff, K., and Schmidhuber, J. (2015). Highway Networks. arXiv.
97. Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
98. Zagoruyko, S., and Komodakis, N. (2016, January 19–22). Wide Residual Networks. Proceedings of the British Machine Vision Conference (BMVC), York, UK.10.5244/C.30.87 ↗
99. Sabour, S., Frosst, N., and Hinton, G.E. (2017). Dynamic Routing between Capsules. arXiv.
100. Quang, N.V., Chun, J., and Tokuyama, T. (2019, January 14–18). CapsuleNet for Micro-Expression Recognition. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France.10.1109/FG.2019.8756544 ↗