Built on

Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning 8
1992
Earlier work this paper cites.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
Original
2013
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)
Original
2014
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision. pp. 1026–1034 (2015)
2015
Earlier work this paper cites.
Mnih, V., Kavukcuoglu, K., Silver, D., et al.: Human-level control through deep reinforcement learning. Nature 518
2015
Earlier work this paper cites.
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2015)
2015
Earlier work this paper cites.
Bojarski, M., Testa, D.D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., Zieba, K.: End to end learning for self-driving cars. CoRR abs/1604.07316
Original
2016
Earlier work this paper cites.
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533 (2016)
Original
2016
Earlier work this paper cites.
Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. arXiv preprint arXiv:1611.02770 (2016)
Original
2016
Earlier work this paper cites.
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P). pp. 372–387 (March 2016). https://doi.org/10.1109/EuroSP.2016.36
2016
Earlier work this paper cites.
Schulman, J., Moritz, P., Levine, S., Jordan, M., Abbeel, P.: High-dimensional continuous control using generalized advantage estimation. In: International Conference on Learning Representations (2016)
2016
Earlier work this paper cites.

Similar

Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. nature 529
2016
Cited alongside, same era.
Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP). pp. 39–57 (May 2017). https://doi.org/10.1109/SP.2017.49
2017
Cited alongside, same era.
Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017)
Original
2017
Cited alongside, same era.
Clemente, A.V., Castejón, H.N., Chandra, A.: Efficient Parallel Methods for Deep Reinforcement Learning. arXiv preprint arXiv:1705.04862 (May 2017)
Original
2017
Cited alongside, same era.

Then

Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. arXiv preprint arXiv:1802.00420 (2018)
Original
2018
Later among the works it cites.
Carlini, N., Wagner, D.: Audio adversarial examples: Targeted attacks on speech-to-text. In: 2018 IEEE Security and Privacy Workshops (SPW). pp. 1–7. IEEE (2018)
2018
Later among the works it cites.
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: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018. pp. 1625–1634 (2018). https://doi.org/10.1109/CVPR.2018.00175
2018
Later among the works it cites.
Li, S., Neupane, A., Paul, S., Song, C., Krishnamurthy, S.V., Chowdhury, A.K.R., Swami, A.: Adversarial perturbations against real-time video classification systems. arXiv preprint arXiv:1807.00458 (2018)
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2019

TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents

Kiourti, Panagiota, Wardega, Kacper, Jha, Susmit et al.

Understand

Recent work has identified that classification models implemented as neural networks are vulnerable to data-poisoning and Trojan attacks at training time.

In this work, we show that these training-time vulnerabilities extend to deep reinforcement learning (DRL) agents and can be exploited by an adversary with access to the training process.
In particular, we focus on Trojan attacks that augment the function of reinforcement learning policies with hidden behaviors.
We demonstrate that such attacks can be implemented through minuscule data poisoning (as little as 0.025% of the training data) and in-band reward modification that does not affect the reward on normal inputs.

Built on

Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning 8
1992
Earlier work this paper cites.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
Original
2013
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)
Original
2014
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision. pp. 1026–1034 (2015)
2015
Earlier work this paper cites.
Mnih, V., Kavukcuoglu, K., Silver, D., et al.: Human-level control through deep reinforcement learning. Nature 518
2015
Earlier work this paper cites.
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2015)
2015
Earlier work this paper cites.
Bojarski, M., Testa, D.D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., Zieba, K.: End to end learning for self-driving cars. CoRR abs/1604.07316
Original
2016
Earlier work this paper cites.
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533 (2016)
Original
2016
Earlier work this paper cites.
Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. arXiv preprint arXiv:1611.02770 (2016)
Original
2016
Earlier work this paper cites.
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P). pp. 372–387 (March 2016). https://doi.org/10.1109/EuroSP.2016.36
2016
Earlier work this paper cites.
Schulman, J., Moritz, P., Levine, S., Jordan, M., Abbeel, P.: High-dimensional continuous control using generalized advantage estimation. In: International Conference on Learning Representations (2016)
2016
Earlier work this paper cites.

Similar

Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. nature 529
2016
Cited alongside, same era.
Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP). pp. 39–57 (May 2017). https://doi.org/10.1109/SP.2017.49
2017
Cited alongside, same era.
Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017)
Original
2017
Cited alongside, same era.
Clemente, A.V., Castejón, H.N., Chandra, A.: Efficient Parallel Methods for Deep Reinforcement Learning. arXiv preprint arXiv:1705.04862 (May 2017)
Original
2017
Cited alongside, same era.

Then

Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. arXiv preprint arXiv:1802.00420 (2018)
Original
2018
Later among the works it cites.
Carlini, N., Wagner, D.: Audio adversarial examples: Targeted attacks on speech-to-text. In: 2018 IEEE Security and Privacy Workshops (SPW). pp. 1–7. IEEE (2018)
2018
Later among the works it cites.
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: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018. pp. 1625–1634 (2018). https://doi.org/10.1109/CVPR.2018.00175
2018
Later among the works it cites.
Li, S., Neupane, A., Paul, S., Song, C., Krishnamurthy, S.V., Chowdhury, A.K.R., Swami, A.: Adversarial perturbations against real-time video classification systems. arXiv preprint arXiv:1807.00458 (2018)
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Gu, T., Dolan-Gavitt, B., Garg, S.: Badnets: Identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733 (2017)

Original

2017

Cited alongside, same era.

Huang, S., Papernot, N., Goodfellow, I., Duan, Y., Abbeel, P.: Adversarial attacks on neural network policies. arXiv preprint arXiv:1702.02284 (2017)

Original

2017

Cited alongside, same era.

Lin, Y.C., Hong, Z.W., Liao, Y.H., Shih, M.L., Liu, M.Y., Sun, M.: Tactics of adversarial attack on deep reinforcement learning agents. arXiv preprint arXiv:1703.06748 (2017)

Original

2017

Cited alongside, same era.

Lu, J., Sibai, H., Fabry, E.: Adversarial examples that fool detectors. arXiv preprint arXiv:1712.02494 (2017)

Original

2017

Cited alongside, same era.

Machado, M.C., Bellemare, M.G., Talvitie, E., Veness, J., Hausknecht, M.J., Bowling, M.: Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents. CoRR abs/1709.06009

Original

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: ACCS’17 (2017)

2017

Cited alongside, same era.

Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M.L., Stolcke, A., Yu, D., Zweig, G.: Toward human parity in conversational speech recognition. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) 25

2017

Cited alongside, same era.

2018