Fetching the paper…
Reading the bibliography…
Patch-based attacks introduce a perceptible but localized change to the input that induces misclassification.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2009)
2009
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Gatys, L., Ecker, A.S., Bethge, M.: Texture synthesis using convolutional neural networks. In: Advances in neural information processing systems (2015)
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Fawzi, A., Frossard, P.: Measuring the effect of nuisance variables on classifiers. In: British Machine Vision Conference (2016)
2016
Earlier work this paper cites.
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (2017)
2017
Earlier work this paper cites.
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: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Hosseini, H., Xiao, B., Poovendran, R.: Google’s cloud vision api is not robust to noise. In: 2017 16th IEEE International Conference on Machine Learning and Applications (2017)
2017
Earlier work this paper cites.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2017)
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: Proceedings of the 2017 ACM on Asia conference on computer and communications security (2017)
2017
Cited alongside, same era.
Pei, K., Cao, Y., Yang, J., Jana, S.: Deepxplore: Automated whitebox testing of deep learning systems. In: proceedings of the 26th Symposium on Operating Systems Principles (2017)
2017
Cited alongside, same era.
Ren, Z., Wang, X., Zhang, N., Lv, X., Li, L.J.: Deep reinforcement learning-based image captioning with embedding reward. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2017)
2017
Cited alongside, same era.
Alzantot, M., Sharma, Y., Chakraborty, S., Zhang, H., Hsieh, C.J., Srivastava, M.B.: Genattack: Practical black-box attacks with gradient-free optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Chernikova, A., Oprea, A., Nita-Rotaru, C., Kim, B.: Are self-driving cars secure? evasion attacks against deep neural networks for steering angle prediction. In: 2019 IEEE Security and Privacy Workshops (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision (2017)
2017
Cited alongside, same era.
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2017)
2017
Cited alongside, same era.
Bhagoji, A.N., He, W., Li, B., Song, D.: Practical black-box attacks on deep neural networks using efficient query mechanisms. In: European Conference on Computer Vision. Springer (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., Li, J.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2018)
2018
Cited alongside, same era.
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., Song, D.: Robust physical-world attacks on deep learning visual classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Later among the works it cites.
Naseer, M.M., Khan, S.H., Khan, M.H., Khan, F.S., Porikli, F.: Cross-domain transferability of adversarial perturbations. In: Advances in Neural Information Processing Systems (2019)
2019
Later among the works it cites.
Naseer, M., Khan, S., Porikli, F.: Local gradients smoothing: Defense against localized adversarial attacks. In: 2019 IEEE Winter Conference on Applications of Computer Vision (2019)
2019
Later among the works it cites.
Ranjan, A., Janai, J., Geiger, A., Black, M.J.: Attacking optical flow. In: Proceedings of the IEEE International Conference on Computer Vision (2019)
2019
Later among the works it cites.
Shi, Y., Wang, S., Han, Y.: Curls & whey: Boosting black-box adversarial attacks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Tu, C.C., Ting, P., Chen, P.Y., Liu, S., Zhang, H., Yi, J., Hsieh, C.J., Cheng, S.M.: Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019)
2019
Later among the works it cites.
Xie, C., Wu, Y., Maaten, L.v.d., Yuille, A.L., He, K.: Feature denoising for improving adversarial robustness. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
2019
Later among the works it cites.
Xie, C., Zhang, Z., Zhou, Y., Bai, S., Wang, J., Ren, Z., Yuille, A.L.: Improving transferability of adversarial examples with input diversity. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2019)
2019
Later among the works it cites.
Clarifai api (2020), https://clarifai.com/
2020
Closest in time.
Google vision api (2020), https://cloud.google.com/vision/
2020
Closest in time.
Goodman, D.: Transferability of adversarial examples to attack cloud-based image classifier service. arXiv pp. arXiv–2001 (2020)
2020
Closest in time.
Huang, L., Gao, C., Zhou, Y., Xie, C., Yuille, A.L., Zou, C., Liu, N.: Universal physical camouflage attacks on object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)
2020
Closest in time.
Li, Y., Bai, S., Zhou, Y., Xie, C., Zhang, Z., Yuille, A.: Learning transferable adversarial examples via ghost networks. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)
2020
Closest in time.