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Deep Neural Networks are well known to be vulnerable to adversarial attacks and backdoor attacks, where minor modifications on the input are able to mislead the models to give wrong results.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook
Nicolas Pinto, Zak Stone, Todd Zickler, and David Cox · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Achieving human parity in conversational speech recognition
Wayne Xiong, Jasha Droppo, Xuedong Huang, Frank Seide, Mike Seltzer, Andreas Stolcke, Dong Yu, and Geoffrey Zweig · 2016
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Frequent subgraph based familial classification of android malware
Ming Fan, Jun Liu, Xiapu Luo, Kai Chen, Tianyi Chen, Zhenzhou Tian, Xiaodong Zhang, Qinghua Zheng, and Ting Liu · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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Adversary resistant deep neural networks with an application to malware detection
Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G Ororbia, Xinyu Xing, Xue Liu, and C Lee Giles · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
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Generative adversarial trainer: Defense to adversarial perturbations with gan
Hyeungill Lee, Sungyeob Han, and Jungwoo Lee · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Discovering adversarial examples with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
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Detecting adversarial samples from artifacts
Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner · 2017
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Mitigating evasion attacks to deep neural networks via region-based classification
Xiaoyu Cao and Neil Zhenqiang Gong · 2017
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Graph embedding based familial analysis of android malware using unsupervised learning
Ming Fan, Xiapu Luo, Jun Liu, Meng Wang, Chunyin Nong, Qinghua Zheng, and Ting Liu · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
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Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks
Huili Chen, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar · 2019
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Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems
Wenbo Guo, Lun Wang, Xinyu Xing, Min Du, and Dawn Song · 2019
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Defending neural backdoors via generative distribution modeling
Ximing Qiao, Yukun Yang, and Hai Li · 2019
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Android malware familial classification and representative sample selection via frequent subgraph analysis
Ming Fan, Jun Liu, Xiapu Luo, Kai Chen, Zhenzhou Tian, Qinghua Zheng, and Ting Liu · 2018
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Adversarial attacks against medical deep learning systems
Samuel G Finlayson, Hyung Won Chung, Isaac S Kohane, and Andrew L Beam · 2018
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Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Uri Shaham, Yutaro Yamada, and Sahand Negahban · 2018
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Slavin Ross and Finale Doshi-Velez · 2018
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Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten · 2018
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Deflecting adversarial attacks with pixel deflection
Aaditya Prakash, Nick Moran, Solomon Garber, Antonella DiLillo, and James Storer · 2018
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Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2018
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Detecting ai trojans using meta neural analysis
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A Gunter, and Bo Li · 2019
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Abs: Scanning neural networks for back-doors by artificial brain stimulation
Yingqi Liu, Wen-Chuan Lee, Guanhong Tao, Shiqing Ma, Yousra Aafer, and Xiangyu Zhang · 2019
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Bypassing backdoor detection algorithms in deep learning
Te Juin Lester Tan and Reza Shokri · 2019
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Robust anomaly detection and backdoor attack detection via differential privacy
Min Du, Ruoxi Jia, and Dawn Song · 2019
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Strip: A defence against trojan attacks on deep neural networks
Yansong Gao, Change Xu, Derui Wang, Shiping Chen, Damith C Ranasinghe, and Surya Nepal · 2019
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
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Latent backdoor attacks on deep neural networks
Yuanshun Yao, Huiying Li, Haitao Zheng, and Ben Y Zhao · 2019
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Unrestricted adversarial examples via semantic manipulation
Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, and David A Forsyth · 2019
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Detecting adversarial perturbations through spatial behavior in activation spaces
Ziv Katzir and Yuval Elovici · 2019
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Adversarial sample detection for deep neural network through model mutation testing
Jingyi Wang, Guoliang Dong, Jun Sun, Xinyu Wang, and Peixin Zhang · 2019
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Deepsec: A uniform platform for security analysis of deep learning model
Xiang Ling, Shouling Ji, Jiaxu Zou, Jiannan Wang, Chunming Wu, Bo Li, and Ting Wang · 2019
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Improving adversarial robustness requires revisiting misclassified examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu · 2019
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A tale of evil twins: Adversarial inputs versus poisoned models
Ren Pang, Hua Shen, Xinyang Zhang, Shouling Ji, Yevgeniy Vorobeychik, Xiapu Luo, Alex Liu, and Ting Wang · 2020
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On the trade-off between adversarial and backdoor robustness
Cheng-Hsin Weng, Yan-Ting Lee, and Shan-Hung Brandon Wu · 2020
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Reflection backdoor: A natural backdoor attack on deep neural networks
Yunfei Liu, Xingjun Ma, James Bailey, and Feng Lu · 2020
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Adversarial camouflage: Hiding physical-world attacks with natural styles
Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A Kai Qin, and Yun Yang · 2020
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Input-aware dynamic backdoor attack
Anh Nguyen and Anh Tran · 2020
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Hidden trigger backdoor attacks
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash · 2020
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Improving adversarial robustness via channel-wise activation suppressing
Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, and Yisen Wang · 2021
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Neural attention distillation: Erasing backdoor triggers from deep neural networks
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma · 2021
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