Summary and Contributions: The paper proposes an input-dependent trigger generation method for backdoor attacks. Cross-trigger loss is explicitly minimized so that one trigger is only effective in one image. This new method allows the attack to bypass a wide range of defense algorithms.
Strengths: The key innovation of the attack algorithm is well presented. The experiments well support the effectiveness of the attack.
Weaknesses: There lacks of comparison with existing dynamic backdoor attacks. The paper does not present how the proposed attack outperforms existing methods.
Correctness: Yes, the methods are correct.
Clarity: Yes, the paper is well written.
Relation to Prior Work: No clear discussion about previous backdoor attacks.
Additional Feedback: The paper is clearly written and well demonstrates the effectiveness of the proposed attack. However, because of the ability of arbitrarily modifying the model, backdoor attacks are known to be hard to defend. Most backdoor attacks do not have an effective defense. In particular, the idea of using dynamically generated backdoor triggers to bypass defenses is not entirely new. For example  generates trigger patterns using auxiliary images, which results in creating non-repeating attacks, similar to this paper. Without comparing with existing backdoor attack methods and providing certain scenarios where the proposed method outperforms existing methods, it is difficult to conclude the contribution of this work.  Ji, Y., Liu, Z., Hu, X., Wang, P. and Zhang, Y., 2019. Programmable Neural Network Trojan for Pre-Trained Feature Extractor. arXiv preprint arXiv:1901.07766.
Summary and Contributions: This paper proposes an approach to train a backdoor attack generator that can generate different backdoor triggers for different input, and the same trigger only works for a specific input. The author design unique evaluation metrics for the work. Experiments show that the proposed framework works on different datasets with different models.
Strengths: 1. The problem setting is novel. 2. The paper is well-written and the logic is clear. 3. Experiment result shows the backdoor attack has a very good successful rate.
Weaknesses: My major concern about this paper is the necessity of the proposed approach. The whole paper is based on the importance of dynamic triggers. However, as the authors mentioned, "dynamic triggers are hard to differentiate from adversarial noise perturbations". From this angle, it seems that any algorithm that efficiently generates adversarial sample can be served as a dynamic backdoor algorithm, with no poisoning samples inserted, and there has been a very rich literature about how to generate adversarial samples. The difference is that this paper requires the backdoor of each input to be different. However, because the input space is large, adversarial samples trivially satisfy the requirement. If the authors believe their dynamic backdoor has unique value, they should provide more justification against this trivial substitution. Another concern is that the added backdoor is very visible according to Figure 4. -------------------------------------- After rebuttal The authors briefly answers my concerns. However, I'm still unconvinced about the realistic value of the method, which is the reason that I ask those questions. The threat model of this paper is stronger than the threat model of evasive attacks such as adversarial samples, so I will hope to see the method can achieve stronger result. However, while the attack success rate is good, the sample generated is very visible, and also the process is costly compare to some lightweight attack models.
Correctness: Yes, I think the method is sound.
Relation to Prior Work: Yes.
Additional Feedback: I believe this paper has a wrong subject area. It should be in "Social Aspects of Machine Learning -> Privacy, Anonymity, and Security".
Summary and Contributions: This paper proposed a novel backdoor attack, which uses an input-aware dynamic trigger pattern through an input-conditioned generator during model training. The resulting backdoor models are more stealthy and difficult to mitigate the adversarial effects. The proposed attack can better evade several model and test-time defenses as well as vision interpretability tools, including Fine-Tuning, Neural-Cleanse, STRIP, and GradCAM. The key invention includes the novel use of diversity and uniqueness in generating trigger patterns Overall, the proposed attack is novel and is a stronger backdoor attack. The findings that its dynamic trigger pattern weakens many defenses and detection methods suggest a new and stronger attack for robustness evaluation, a big leap beyond using a universal trigger pattern.
Strengths: 1. Novelty: the proposed backdoor attack uses input-specific dynamic trigger pattern instead of conventionally used universal trigger pattern. The proposal of diversity and uniqueness (nonreusability) is also pretty novel. 2. The results of weakening several defenses give significant contributions. This attack has the potential of serving as a new baseline for robustness evaluation against backdoor attacks. 3. The experiments are thorough and convincing.
Weaknesses: I hope to see the following updates and discussions in the rebuttal, and I am happy to increase my review rating once my concerns are addressed. 1. In the ICLR publication "Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness", it was shown that Fine-Tuning is not the most effective approach to recover backdoor models, unless one has a sufficient amount of clean data to alleviate the backdoor effect. I would like to see how well the proposed attack is against the mode connectivity based defense proposed in the ICLR paper. 2. When reporting the attack/clean accuracy, it was unclear whether the authors were using data samples (and how many) from training data or testing data (unseen when training the backdoor model). I also would like the authors to address the robustness of the proposed approach when performing backdoor attack on testing (unseen) data. Intuitively, universal trigger pattern should be more robust to distribution shifts between training and testing data, and the input-aware apporach can be more sensitive (and cause attack to fail) if the generator cannot overcome the inherent distribution shift. Also, using testing (untrained) data makes more sense in all experiments. I hope the authors can clarity the data setting. 3.The probabilistic backdooring during model training according to Eq. (2) is worthy of more exploration. The authors only used rho_a=rho_c=0.1 in the experiments. I hope to see some parameter sensitivity analysis on these two parameters.
Correctness: Mostly correct and clearly explained in the supplementary material (there are some referencing issues in the supplementary document). The authors need to veritfy the reported results are using training data or testing (unseen data during training), and are suggested to test against more advanced defenses.
Clarity: This paper is well written and easy to follow.
Relation to Prior Work: The authors are suggested to test the robustness against more recent defenses, such as "Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness".
Additional Feedback: Post-rebuttal comments: I thank the authors for providing additional results and clarification. My questions have been fully addressed, and I will increase my overall score. I hope the authors can incorporate the new results in the revised version.