Research on the robustness of convolutional neural networks in image recognition

Dian LIN; Li PAN; Ping YI

doi:10.11959/j.issn.2096-109x.2022037

您当前的位置：

首页 >

文章列表页 >

Research on the robustness of convolutional neural networks in image recognition

Papers | 更新时间：2024-06-03

- Research on the robustness of convolutional neural networks in image recognition
- Chinese Journal of Network and Information Security Vol. 8, Issue 3, Pages: 111-122(2022)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2022037
  CLC： TP183
- Online First：2022-06，
  
  Published：15 June 2022
- 稿件说明：
移动端阅览
Dian LIN, Li PAN, Ping YI. Research on the robustness of convolutional neural networks in image recognition[J]. Chinese Journal of Network and Information Security, 2022, 8(3): 111-122.
DOI：

Dian LIN, Li PAN, Ping YI. Research on the robustness of convolutional neural networks in image recognition[J]. Chinese Journal of Network and Information Security, 2022, 8(3): 111-122. DOI： 10.11959/j.issn.2096-109x.2022037.

摘要

卷积神经网络是目前人工智能领域在图像识别与处理相关应用中的关键技术之一，广泛的应用使对其鲁棒性研究的重要性不断凸显。以往对于卷积神经网络鲁棒性的研究较为笼统，且多集中在对抗鲁棒性方面。这难以更深入地研究神经网络鲁棒性的发生机制，已经不适应人工智能的发展。引入神经科学的相关研究，提出了视觉鲁棒性的概念，通过研究神经网络模型与人类视觉系统的相似性，揭示了神经网络鲁棒性的内在缺陷。回顾了近年来神经网络鲁棒性的研究现状，并分析了神经网络模型缺乏鲁棒性的原因。神经网络缺乏鲁棒性体现在其对于微小扰动的敏感性，其原因在于神经网络会更倾向于学习人类难以感知的高频信息用于计算和推理。而这部分高频信息很容易被扰动所破坏，最终导致模型出现判断错误。传统鲁棒性的研究大多关注模型的数学性质，无法突破神经网络的天然局限性。视觉鲁棒性在传统鲁棒性的概念上进行拓展。传统鲁棒性概念衡量模型对于失真变形的图像样本的辨识能力，失真样本与原始干净样本在鲁棒模型上都能保持正确的输出。视觉鲁棒性衡量模型与人类判别能力的一致性。这需要将神经科学和心理学的研究方法、成果与人工智能相结合。回顾了神经科学在视觉领域的发展，讨论了认知心理学的研究方法在神经网络鲁棒性研究上的应用。人类视觉系统在学习和抽象能力上具有优势，神经网络模型在计算和记忆速度方面强于人类。人脑的生理结构与神经网络模型的逻辑结构的差异是导致神经网络鲁棒性问题的关键因素。视觉鲁棒性的研究需要对人类的视觉系统有更深刻的理解。揭示人类视觉系统与神经网络模型在认知机制上的差异，并对算法进行有效的改进，这是神经网络鲁棒性乃至人工智能算法的主要发展趋势。

Abstract

Convolutional neural network is one of the key technologies in the application of image recognition and processing in artificial intelligence.Its wide application makes researches on its robustness more and more important.Previous researches on robustness of neural networks were too sweeping and most of them focused on adversarial robustness

which causes difficulty in further study in the mechanism of neural network robustness.The related researches of neuroscience were introduced and the concept of visual robustness was put forward.By studying the similarity and difference between neural network models and human visual system

the internal mechanism and faults of neural network robustness were revealed.The researches of neural network robustness in recent years were reviewed

and the reasons for the lack of robustness of neural network models were analyzed.The lack of robustness of neural networks is reflected in their sensitivity to small perturbations.The reason is that neural networks tend to learn high-frequency information for calculation and inference

which is difficult for humans to recognize.High-frequency information is easily affected by perturbations

and eventually causes mistakes of models.Previous researches on robustness mostly focused on mathematical properties of models

and were limited in the natural faults of neural networks.Visual robustness extends the traditional concept of robustness.The traditional concept of robustness measures the discrimination ability of models for distorted image examples.Distorted examples and clean examples can get correct outputs through robust models.Visual robustness measures the consistency between models and humans in discrimination ability.Visual robustness combines the research methods and achievements of neuroscience and psychology with artificial intelligence.The development of neuroscience in the field of vision were reviewed

and the application of research methods of cognitive psychology in neural network robustness were discussed.Human visual system has advantages in learning and abstract ability

whill neural network models have better performance in calculation speed and memory.The difference between the physiological structure of human brain and the logical structure of neural network models is the key factor leading to the problem of robustness of neural networks.The research of visual robustness requires deeper understanding of human visual system.Revealing the differences in cognitive mechanism between human visual system and neural network models and effectively improving the algorithm are the development trends of neural network robustness and even artificial intelligence.

关键词

Keywords

references

LECUN Y , BOTTOU L , BENGIO Y , et al . Gradient-based learning applied to document recognition [J ] . Proceedings of the IEEE , 1998 , 86 ( 11 ): 2278 - 2324 .

KRIZHEVSKY A , SUTSKEVER I , HINTON G . ImageNet classification with deep convolutional neural networks [J ] // Communications of the ACM , 2017 , 60 : 84 - 90 .

SZEGEDY C , ZAREMBA W , SUTSKEVER I , et al . Intriguing properties of neural networks [C ] // 2nd International Conference on Learning Representations . ICLR 2014 .

GOODFELLOW I J , SHLENS J , SZEGEDY C . Explaining and harnessing adversarial examples [C ] // 3rd International Conference on Learning Representations . ICLR , 2015 .

CARLINI N , WAGNER D . Towards evaluating the robustness of neural networks [C ] // 2017 IEEE Symposium on Security and Privacy (SP) . IEEE , 2017 : 39 - 57 .

MADRY A , MAKELOV A , SCHMIDT L , et al . Towards deep learning models resistant to adversarial attacks [C ] // International Conference on Learning Representa-tions . 2018 .

PAPERNOT N , MCDANIEL P , GOODFELLOW I , et al . Practical black-box attacks against machine learning [C ] // Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security . 2017 : 506 - 519 .

LIU Y , CHEN X , LIU C , et al . Delving into transferable adversarial examples and black-box attacks [C ] // International Conference on Learning Representations . 2017 .

BRENDEL W , RAUBER J , BETHGE M . Decision-based adversarial attacks:reliable attacks against black-box machine learning models [C ] // International Conference on Learning Representations . 2018 .

DZIUGAITE G K , GHAHRAMANI Z , ROY D M . A study of the effect of jpg compression on adversarial images [J ] . arXiv preprint arXiv:1608.00853 , 2016 .

VINCENT P , LAROCHELLE H , BENGIO Y , et al . Extracting and composing robust features with denoising autoencod-ers [C ] // Proceedings of the 25th International Conference on Machine Learning . 2008 : 1096 - 1103 .

XU W , EVANS D , QI Y . Feature squeezing:Detecting adversarial examples in deep neural networks [J ] . arXiv preprint arXiv:1704.01155 , 2017 .

GROSSE K , MANOHARAN P , PAPERNOT N , et al . On the (statistical) detection of adversarial examples [J ] . arXiv pre-print arXiv:1702.06280 , 2017 .

CARLINI N , WAGNER D . Adversarial examples are not easily detected:Bypassing ten detection methods [C ] // Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security . 2017 : 3 - 14 .

SHORTEN C , KHOSHGOFTAAR T M . A survey on image data augmentation for deep learning [J ] . Journal of Big Data , 2019 , 6 ( 1 ): 1 - 48 .

TSIPRAS D , SANTURKAR S , ENGSTROM L , et al . Robustness May Be at Odds with Accuracy [C ] // International Conference on Learning Representations . 2019 .

XIE C , TAN M , GONG B , et al . Adversarial examples improve image recognition [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2020 : 819 - 828 .

REDDY M V , BANBURSKI A , PANT N , et al . Biologically Inspired Mechanisms for Adversarial Robustness [J ] . Advances in Neural Information Processing Systems , 2020 ,33.

KIM E , REGO J , WATKINS Y , et al . Modeling biological immunity to adversarial examples [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2020 : 4666 - 4675 .

FAWZI A , FROSSARD P . Manitest:Are classifiers really invariant? [C ] // British Machine Vision Conference (BMVC) . 2015 : 106.1 - 106.13 .

LENC K , VEDALDI A . Understanding image representations by measuring their equivariance and equivalence [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2015 : 991 - 999 .

HINTON G E , KRIZHEVSKY A , WANG S D . Transforming auto-encoders [C ] // International Conference on Artificial Neural Networks . Springer,Berlin,Heidelberg , 2011 : 44 - 51 .

PATRICK M K , ADEKOYA A F , MIGHTY A A , et al . Capsule networks–a survey [J ] . Journal of King Saud University-Computer and Information Sciences , 2022 , 34 ( 1 ): 1295 - 1310 .

PHONG N H , RIBEIRO B . Advanced capsule networks via context awareness [J ] . Lecture Notes in Computer Science . 2019 : 166 - 177 .

RAJASEGARAN J , JAYASUNDARA V , JAYASEKARA S , et al . Deepcaps:Going deeper with capsule networks [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2019 : 10725 - 10733 .

HENDRYCKS D , DIETTERICH T . Benchmarking Neural Network Robustness to Common Corruptions and Perturbations [C ] // International Conference on Learning Representations . 2018 .

PAPERNOT N , MCDANIEL P , WU X , et al . Distillation as a defense to adversarial perturbations against deep neural networks [C ] // 2016 IEEE Symposium on Security and Privacy (SP) . IEEE , 2016 : 582 - 597 .

ATHALYE A , CARLINI N , WAGNER D . Obfuscated gradients give a false sense of security:Circumventing defenses to adversarial examples [C ] // International Conference on Machine Learning . PMLR , 2018 : 274 - 283 .

MCDANIEL P , PAPERNOT N , CELIK Z B . Machine learning in adversarial settings [J ] . IEEE Security ＆ Privacy , 2016 , 14 ( 3 ): 68 - 72 .

STUTZ D , HEIN M , SCHIELE B . Disentangling adversarial robustness and generalization [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2019 : 6976 - 6987 .

ILYAS A , SANTURKAR S , ENGSTROM L , et al . Adversarial examples are not bugs,they are features [J ] . Advances in Neural Information Processing Systems , 2019 ,32.

NGUYEN A , YOSINSKI J , CLUNE J . Deep neural networks are easily fooled:High confidence predictions for unrecognizable images [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2015 : 427 - 436 .

TRAMER F , CARLINI N , BRENDEL W , et al . On adaptive attacks to adversarial example defenses [J ] . Advances in Neural Information Processing Systems , 2020 , 33 : 1633 - 1645 .

GEIRHOS R , MEDINA TEMME C R , RAUBER J , et al . Generalisation in humans and deep neural networks [C ] // Thirty-second Annual Conference on Neural Information Processing Systems 2018 (NeurIPS 2018) . Curran , 2019 : 7549 - 7561 .

ZHENG S , SONG Y , LEUNG T , et al . Improving the robustness of deep neural networks via stability training [C ] // Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition . 2016 : 4480 - 4488 .

HOSSEINI H , POOVENDRAN R . Semantic adversarial examples [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops . 2018 : 1614 - 1619 .

HUBEL D H , WIESEL T N . Receptive fields,binocular interaction and functional architecture in the cat's visual cortex [J ] . The Journal of physiology , 1962 , 160 ( 1 ): 106 - 154 .

GOODALE M A , MILNER A D . Separate visual pathways for perception and action [J ] . Trends in Neurosciences , 1992 , 15 ( 1 ): 20 - 25 .

KATZNER S , WEIGELT S . Visual cortical networks:of mice and men [J ] . Current Opinion in Neurobiology , 2013 , 23 ( 2 ): 202 - 206 .

RAJALINGHAM R , DICARLO J J . Reversible inactivation of different millimeter-scale regions of primate IT results in different patterns of core object recognition deficits [J ] . Neuron , 2019 , 102 ( 2 ): 493 - 505 .e5.

KANWISHER N . Functional specificity in the human brain:a window into the functional architecture of the mind [J ] . Proceedings of the National Academy of Sciences , 2010 , 107 ( 25 ): 11163 - 11170 .

KONKLE T , OLIVA A . A real-world size organization of object responses in occipitotemporal cortex [J ] . Neuron , 2012 , 74 ( 6 ): 1114 - 1124 .

KRIEGESKORTE N , MUR M , RUFF D A , et al . Matching categorical object representations in inferior temporal cortex of man and monkey [J ] . Neuron , 2008 , 60 ( 6 ): 1126 - 1141 .

PROKLOVA D , KAISER D , PEELEN M V . Disentangling representations of object shape and object category in human visual cortex:The animate–inanimate distinction [J ] . Journal of Cognitive Neuroscience , 2016 , 28 ( 5 ): 680 - 692 .

FISHER R A . The use of multiple measurements in taxonomic problems [J ] . Annals of Eugenics , 1936 , 7 ( 2 ): 179 - 188 .

MCCULLOCH W S , PITTS W . A logical calculus of the ideas immanent in nervous activity [J ] . The Bulletin of Mathematical Biophysics , 1943 , 5 ( 4 ): 115 - 133 .

EICKENBERG M , GRAMFORT A , VAROQUAUX G , et al . Seeing it all:Convolutional network layers map the function of the human visual system [J ] . Neuro Image , 2017 , 152 : 184 - 194 .

HORIKAWA T , KAMITANI Y . Generic decoding of seen and imagined objects using hierarchical visual features [J ] . Nature Communications , 2017 , 8 ( 1 ): 1 - 15 .

ST-YVES G , NASELARIS T . The feature-weighted receptive field:an interpretable encoding model for complex feature spaces [J ] . Neuro Image , 2018 , 180 : 188 - 202 .

WEN H , SHI J , ZHANG Y , et al . Neural encoding and decoding with deep learning for dynamic natural vision [J ] . Cerebral Cortex , 2018 , 28 ( 12 ): 4136 - 4160 .

CADIEU C F , HONG H , YAMINS D L K , et al . Deep neural networks rival the representation of primate IT cortex for core visual object recognition [J ] . PLoS Comput Biol , 2014 , 10 ( 12 ): e1003963 .

BASHIVAN P , KAR K , DICARLO J J . Neural population control via deep image synthesis [J ] . Science , 2019 , 364 ( 6439 ).

ULLMAN S , ASSIF L , FETAYA E , et al . Atoms of recognition in human and computer vision [J ] . Proceedings of the National Academy of Sciences , 2016 , 113 ( 10 ): 2744 - 2749 .

ELSAYED G , SHANKAR S , CHEUNG B , et al . Adversarial examples that fool both computer vision and time-limited humans [C ] // Advances in Neural Information Processing Systems . 2018 : 3910 - 3920 .

ZHOU Z , FIRESTONE C . Humans can decipher adversarial images [J ] . Nature Communications , 2019 , 10 ( 1 ).

SANTURKAR S , TSIPRAS D , TRAN B , et al . Image synthesis with a single (robust) classifier [J ] . Advances in Neural Information Processing Systems , 2019 ,32.

RITTER S , BARRETT D G T , SANTORO A , et al . Cognitive psychology for deep neural networks:A shape bias case study [C ] // International Conference on Machine Learning . PMLR , 2017 : 2940 - 2949 .

HOSSEINI H , XIAO B , JAISWAL M , et al . Assessing shape bias property of convolutional neural networks [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops . 2018 : 1923 - 1931 .

GEIRHOS R , RUBISCH P , MICHAELIS C , et al . ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness [C ] // International Conference on Learning Representations , ICLR 2019 .

Views

674

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Cross-representation transferable adversarial examples generation for audio classification

Robust Schnorr-based subgroup multi-signature scheme

Robust deepfake detection method based on siamese network

Unbalanced protocol recognition method based on improved residual U-Net

Survey on adversarial attacks and defense of face forgery and detection

Related Author

ZHANG Liguo

TIAN Ye

LONG Yunfei

TIAN Zilin

WANG Zhiwei

ZHU Qiuchi

ZHANG Zhenqi

LIN Shanhe

Related Institution

Hangzhou Research Institute of Xidian University

College of Computer Science and Technology，Harbin Engineering University

Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications

School of Computer Science, Nanjing University of Posts and Telecommunications

Fujian Star-net Communication Company Limited

⁰