Characterizing convolutional neural networks with one-pixel signature
We propose a new representation, one-pixel signature, that can be used to reveal the characteristics of the convolution neural networks (CNNs). Here, each CNN classifier is associated with a signature that is created by generating, pixel-by-pixel, an adversarial value that is the result of the largest change to the class prediction. The one-pixel signature is agnostic to the design choices of CNN architectures such as type, depth, activation function, and how they were trained. It can be computed efficiently for a black-box classifier without accessing the network parameters. Classic networks such as LetNet, VGG, AlexNet, and ResNet demonstrate different characteristics in their signature images. For application, we focus on the classifier backdoor detection problem where a CNN classifier has been maliciously inserted with an unknown Trojan. We show the effectiveness of the one-pixel signature in detecting backdoored CNN. Our proposed one-pixel signature representation is general and it can be applied in problems where discriminative classifiers, particularly neural network based, are to be characterized.
The paper describes a systemic approach for detecting the type of a classifier based on the computation and use of a per-pixel signature image feature. It claims that a specific classifier, e.g., a VGG, or other type of CNN, will produce a different signature image from another classifier, or from the same classifier when trained with manipulated data, e.g., by an unknown Trojan. Some results are shown that illustrate how this signature feature can be used to differentiate between 4 different CNNs, or when data are manipulated. Although the paper has some merit, in creating and using the specific signature, there is no deeper analysis of why and how this feature can differentiate between classifiers, or of the dependence on the problem under investigation. It cannot be claimed to produce a robust feature that can be applied to quite diverse cases and environments (the inability to handle, for example all-to-all attacks is admitted in the paper). Moreover, the title is misleading, since the method does not necessarily refers to CNNs, but in general to some classifier.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Characterizing convolutional neural networks with one-pixel signature
We propose a new representation, one-pixel signature, that can be used to reveal the characteristics of the convolution neural networks (CNNs). Here, each CNN classifier is associated with a signature that is created by generating, pixel-by-pixel, an adversarial value that is the result of the largest change to the class prediction. The one-pixel signature is agnostic to the design choices of CNN architectures such as type, depth, activation function, and how they were trained. It can be computed efficiently for a black-box classifier without accessing the network parameters. Classic networks such as LetNet, VGG, AlexNet, and ResNet demonstrate different characteristics in their signature images. For application, we focus on the classifier backdoor detection problem where a CNN classifier has been maliciously inserted with an unknown Trojan. We show the effectiveness of the one-pixel signature in detecting backdoored CNN. Our proposed one-pixel signature representation is general and it can be applied in problems where discriminative classifiers, particularly neural network based, are to be characterized.
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>



The paper describes a systemic approach for detecting the type of a classifier based on the computation and use of a per-pixel signature image feature. It claims that a specific classifier, e.g., a VGG, or other type of CNN, will produce a different signature image from another classifier, or from the same classifier when trained with manipulated data, e.g., by an unknown Trojan. Some results are shown that illustrate how this signature feature can be used to differentiate between 4 different CNNs, or when data are manipulated. Although the paper has some merit, in creating and using the specific signature, there is no deeper analysis of why and how this feature can differentiate between classifiers, or of the dependence on the problem under investigation. It cannot be claimed to produce a robust feature that can be applied to quite diverse cases and environments (the inability to handle, for example all-to-all attacks is admitted in the paper). Moreover, the title is misleading, since the method does not necessarily refers to CNNs, but in general to some classifier.
assistant
The paper describes a systemic approach for detecting the type of a classifier based on the computation and use of a per-pixel signature image feature. It claims that a specific classifier, e.g., a VGG, or other type of CNN, will produce a different signature image from another classifier, or from the same classifier when trained with manipulated data, e.g., by an unknown Trojan. Some results are shown that illustrate how this signature feature can be used to differentiate between 4 different CNNs, or when data are manipulated. Although the paper has some merit, in creating and using the specific signature, there is no deeper analysis of why and how this feature can differentiate between classifiers, or of the dependence on the problem under investigation. It cannot be claimed to produce a robust feature that can be applied to quite diverse cases and environments (the inability to handle, for example all-to-all attacks is admitted in the paper). Moreover, the title is misleading, since the method does not necessarily refers to CNNs, but in general to some classifier.