Abstract
Deep Neural Networks (DNN) has found their applications in the real time, for example, facial recognition for security in ATMs and self-driving cars. A major security threat to DNN is through adversarial attacks. An adversarial sample is an image that has been changed in such a way that it is imperceptible to human eye but causes the image to be misclassified by a Convolutional Neural Networks (CNN). The objective of this research work is to devise pixel based algorithms for adversarial attacks on images. For validating the algorithms, untargeted attack is performed on MNIST and CIFAR-10 dataset using techniques such as edge detection, Gradient weighted Class Activation Mapping (GRAD-CAM) and noise addition whereas targeted attack is performed on MNIST dataset using Saliency maps. These adversarial images thus generated are then passed to a CNN model and the misclassification results are analyzed. From the analysis, it has been inferred that it is easier to fool CNNs using untargeted attacks than the targeted attacks. Also, grayscale images (MNIST) are preferred to generate robust adversarial examples compared to colored images (CIFAR-10).
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References
Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: IEEE Symposium on Security and Privacy (SP), pp. 39–57 (2017)
Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., Mukhopadhyay, D.: Adversarial attacks and defences: a survey machine learning arXiv:1810.00069v1 (2018)
Chen, P.Y., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.J.: ZOO: zeroth order optimization based black-box attacks to deep neural, networks without training substitute models. In: ACM Workshop on Artificial Intelligence and Security (AISec@CCS), pp. 15–26 (2017)
Dong, Y., et al.: Boosting adversarial attacks with momentum. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9185–9193 (2018)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations, pp. 1–11 (2015)
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. In: International Conference on Learning Representations, pp. 1–17 (2015)
Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput., 1–12 (2019). https://doi.org/10.1109/TEVC.2019.2890858
Szegedy, C.: Intriguing properties of neural networks. In: International Conference on Learning Representations, pp. 1–10 (2013)
Yuan, X., He, P., Zhu, Q., Li, X.: Adversarial examples: attacks and defences for deep learning. IEEE Trans. Neural Netw. Learn. Syst., 1–20 (2019). https://doi.org/10.1109/TNNLS.2018.2886017
Zhao, Y.S., Zhu, H., Shen, Q., Liang, R., Chen, K., Zhang, S.: Practical adversarial attack against object detector. In: USENIX Security Symposium arXiv:1812.10217v3 (2018)
CIFAR–10, March 2020. https://www.cs.toronto.edu/~kriz/cifar.html
MNIST, March 2020. http://yann.lecun.com/exdb/mnist/
VGG-16 trained on CIFAR-10, April 2020. https://github.com/mjiansun/cifar10-vgg16
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., Madry, A.: Adversarial examples are not bugs, they are features. Computer Vision and Pattern Recognition (2019). arXiv:1905.02175v4
Vargas, A.V., Su, J.: Understanding the one-pixel attack: propagation maps and locality analysis (2019). arXiv:1902.02947v1
Acknowledgements
The authors sincerely thank Dept. of CSE, Sri Sivasubramaniya Nadar College of Engineering for the permission given to utilize the High Performance Computing Laboratory and its GPU server for successful execution of this project.
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Srinivasan, K., Jello Raveendran, P., Suresh, V., Anna Sundaram, N.R. (2021). Pixel Based Adversarial Attacks on Convolutional Neural Network Models. In: Krishnamurthy, V., Jaganathan, S., Rajaram, K., Shunmuganathan, S. (eds) Computational Intelligence in Data Science. ICCIDS 2021. IFIP Advances in Information and Communication Technology, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-030-92600-7_14
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DOI: https://doi.org/10.1007/978-3-030-92600-7_14
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