Machine learning (ML) algorithms, especially deep neural networks, have demonstrated success in several domains. However, several types of attacks have raised concerns about deploying ML in safety-critical domains, such as autonomous driving and security. An attacker perturbs a data point slightly in the concrete feature space (e.g., pixel space) and causes the ML algorithm to produce incorrect output ( a perturbed stop sign is classified as a yield sign). These perturbed data points are called adversarial examples, and there are numerous algorithms in the literature for constructing adversarial examples and defending against them. In this paper we explore semantic adversarial examples (SAEs) where an attacker creates perturbations in the semantic space representing the environment that produces input for the ML model. For example, an attacker can change the background of the image to be cloudier to cause misclassification. We present an algorithm for constructing SAEs that uses recent advances in differential rendering and inverse graphics. The generated SAEs are easily realizable in the physical world, and can be used to help the model adapt better to different domains.
This paper studies the problem of semantic adversarial attacks with a differentiable de-rendering and rendering pipeline. More specifically, this paper proposed a variant of FGSM (Goodfellow et al. 2015) and PGD (Madry et al. 2017) by extending the traditional Lp-bounded adversarial attacks in the rich semantic space. It considered a list of semantic parameters including color, weather, foliage, rotation, transformation, and object shape. To facilitate back-propagation and improve the quality of rendering, this paper re-implemented the differentiable equivalents of several image manipulation operations based on the previous work (Yao et al. 2018). For experimental evaluations, this paper selected the object detector SqueezeDet (We et al. 2016) as the target model for attack on the virtual KITTI dataset. Experiments demonstrated that the generated semantic adversarial examples (SAEs) can attack the SqueezeDet (see Table 1 and Table 2). By re-training with augmented data by the proposed method, the robustness of SqueezeDet (see Table 3) can be further improved.
Overall, this is an okay paper with incremental technical novelty and clear presentation. Reviewer has several concerns regarding the experiments.
(1) This paper only conducts experiments on virtual KITTI dataset, a synthetic benchmark for object detection and semantic segmentation. In general, studying the adversarial examples in the synthetic domain seems not a significant contribution. Reviewer would like to know the performance on the real dataset such as Cityscape (used in Yao et al. 2018) and other challenging indoor datasets such as ADE20K. At least, reviewer would like to know whether re-training on adversarial examples help to improve the performance on real dataset?
(2) The conclusion is largely based on the 1547 semantic adversarial examples generated, while there are more than 4K synthetic images in the dataset. This seems contradicts against the flexibility of generating semantic adversarial examples described in the paper (e.g., single parameter modifications). Reviewer suspects the proposed differentiable rendering pipeline is not very effective so that generating SAEs requires quite a bit exhaustive search over the parameter space. Please comment on the average running time for generating a semantic adversarial example. How does that compare to generating a pixel-based adversarial example?
(3) While several different quantitative analyses have been conducted, this paper only provides two examples as the qualitative result (see Figure 2). It would be more convincing if this paper provides more such examples in the appendix. In addition, ablation studies on semantic parameters are currently missing. Furthermore, reviewer wonders if it is possible to report the FID score and make sure the generated adversarial examples have the same distribution as ground-truth images.
(4) SqueezeDet is the only model used in the paper. Please also consider other models and report the attack performance and transferability. In a high-level, reviewer would like to know whether the proposed differentiable rendering method generalizes to other tasks including semantic segmentation and depth prediction.
(5) The following paper is related (see Figure 5 of MeshAdv paper), but not even mentioned here. Reviewer would like to see the comparison between the proposed method and the MeshAdv baseline.
-- MeshAdv: Adversarial Meshes for Visual Recognition. Xiao et al. In CVPR 2019.