Realistic Adversarial Examples in 3D Meshes
Highly expressive models especially deep neural networks (DNNs) have been widely applied to various applications and achieved increasing success. However, recent studies show that such machine learning models appear to be vulnerable against adversarial examples. So far adversarial examples have been heavily explored for 2D images, while few work has tried to understand the vulnerabilities of 3D objects which exist in real world, where 3D objects are projected to 2D domains by photo taking for different learning (recognition) tasks. In this paper we consider adversarial behaviors in practical scenarios by manipulating the shape and texture of a given 3D mesh representation of an object. Our goal is to project the optimized "adversarial meshes" to 2D with photo-realistic rendering engine, and still able to mislead different machine learning models. Extensive experiments show that by generating unnoticeable 3D adversarial perturbation on shape or texture for a 3D mesh, the corresponding projected 2D instance can either lead classifiers to misclassify the victim object arbitrary malicious target, or hide any target object within the scene from state-of-the-art object detectors. We conduct human studies to show that our optimized adversarial 3D perturbation is highly unnoticeable for human vision systems. In addition to the subtle perturbation on a given 3D mesh, we also propose to synthesize a realistic 3D mesh to put in a scene mimicking similar rendering conditions and therefore attack existing objects within it. In-depth analysis for transferability among different 3D rendering engines and vulnerable regions of meshes are provided to help better understand adversarial behaviors in practice and motivate potential defenses.
 Pros: Studies how perturbations in 3D shape and albedo maps may be used to generate adversarial inputs and evaluates them on densenet and Inception-v3 nets. Cons: The paper should be checked for sentence structure, typos, etc. There are a number of places with linguistic errors. The paper is hard to read. The claims in the contributions section (in page 2) are not adequately supported by extensive experimentation. For instance, the section claims that an in-depth analysis of vulnerable regions in 3D meshes is done in the paper and the discussion in the paper is mainly about a case study visualizing the vertex flow in the given mesh region. While the authors try to cover reasonable ground in terms of studying how adversarial data can be generated starting from mesh data, a systematic in-depth study of each of the factors outlined in the contribution section will strengthen the paper and could offer insights into how the work can be used to improve robustness of classification.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Realistic Adversarial Examples in 3D Meshes
Highly expressive models especially deep neural networks (DNNs) have been widely applied to various applications and achieved increasing success. However, recent studies show that such machine learning models appear to be vulnerable against adversarial examples. So far adversarial examples have been heavily explored for 2D images, while few work has tried to understand the vulnerabilities of 3D objects which exist in real world, where 3D objects are projected to 2D domains by photo taking for different learning (recognition) tasks. In this paper we consider adversarial behaviors in practical scenarios by manipulating the shape and texture of a given 3D mesh representation of an object. Our goal is to project the optimized "adversarial meshes" to 2D with photo-realistic rendering engine, and still able to mislead different machine learning models. Extensive experiments show that by generating unnoticeable 3D adversarial perturbation on shape or texture for a 3D mesh, the corresponding projected 2D instance can either lead classifiers to misclassify the victim object arbitrary malicious target, or hide any target object within the scene from state-of-the-art object detectors. We conduct human studies to show that our optimized adversarial 3D perturbation is highly unnoticeable for human vision systems. In addition to the subtle perturbation on a given 3D mesh, we also propose to synthesize a realistic 3D mesh to put in a scene mimicking similar rendering conditions and therefore attack existing objects within it. In-depth analysis for transferability among different 3D rendering engines and vulnerable regions of meshes are provided to help better understand adversarial behaviors in practice and motivate potential defenses.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 Pros: Studies how perturbations in 3D shape and albedo maps may be used to generate adversarial inputs and evaluates them on densenet and Inception-v3 nets. Cons: The paper should be checked for sentence structure, typos, etc. There are a number of places with linguistic errors. The paper is hard to read. The claims in the contributions section (in page 2) are not adequately supported by extensive experimentation. For instance, the section claims that an in-depth analysis of vulnerable regions in 3D meshes is done in the paper and the discussion in the paper is mainly about a case study visualizing the vertex flow in the given mesh region. While the authors try to cover reasonable ground in terms of studying how adversarial data can be generated starting from mesh data, a systematic in-depth study of each of the factors outlined in the contribution section will strengthen the paper and could offer insights into how the work can be used to improve robustness of classification.
assistant
 Pros: Studies how perturbations in 3D shape and albedo maps may be used to generate adversarial inputs and evaluates them on densenet and Inception-v3 nets. Cons: The paper should be checked for sentence structure, typos, etc. There are a number of places with linguistic errors. The paper is hard to read. The claims in the contributions section (in page 2) are not adequately supported by extensive experimentation. For instance, the section claims that an in-depth analysis of vulnerable regions in 3D meshes is done in the paper and the discussion in the paper is mainly about a case study visualizing the vertex flow in the given mesh region. While the authors try to cover reasonable ground in terms of studying how adversarial data can be generated starting from mesh data, a systematic in-depth study of each of the factors outlined in the contribution section will strengthen the paper and could offer insights into how the work can be used to improve robustness of classification.