reviews: 
Comments: The paper proposes a framework to detect data distribution changes in image classification neural networks by monitoring changes in prediction probability distributions. It claims to identify both the type and magnitude of the distribution shift by combining a classification network (for identifying noise/weather effects) with a quantification-based method that estimates class proportions, though it confuses the concept of drift types with different noise patterns.
Strengths: The paper attempts to provide a comprehensive solution by jointly considering drift detection, magnitude estimation, and handling varying class distributions in a single framework.
Weaknesses: Weaknesses:

1. The first paragraph of the Introduction is entirely devoted to a general introduction of DNNs, without any mention of drift. Given that the paper's core focus is on detecting drift types and drift magnitude, I believe the DNN-related introduction is not central to this paper, and this entire paragraph provides little valuable information to readers.
2. The paper is poorly written. A paper should highlight its key points and quickly convey its innovations to readers. In the introduction, three paragraphs are spent on related work, while only one paragraph describes the paper's contribution, and even this paragraph fails to intuitively explain why the proposed method would work.
3. The first paragraph of Preliminaries is entirely about concept drift. So I assume the paper aims to address concept drift issues. If this is the case, there is a serious misuse of terminology. In concept drift, drift types include abrupt drift, gradual drift, recurrent drift, etc. While this paper uses the term "drift type" 86 times, it never explains or strictly defines what drift type means. According to Table 1 in the paper, the authors treat "gaussian noise, poisson noise, salt noise, snow fog rain, etc" as drift types. I find this inappropriate as these are more like different types of concepts. In summary, drift type is a specialized term in concept drift [1].
4. Line 163 states: "Pi,j denote the prediction probability distribution of the images belonging to the class j predicted as class i", but according to equation 1, I believe p_ij is a scalar, not a distribution. This appears to be an expression issue. I believe the paper consistently misuses the term "distribution".
5. Line 183, "per each drift type" should be changed to "for each effect type".
6. Lines 117-119 state: "To understand the impact of data drifts on image classification neural networks, let us consider the impact of Gaussian noise on a classification network trained on the MNIST handwritten digit image dataset, detailed in Section 4, under the effect of Gaussian noise." This sentence is highly redundant.
7. The experimental evaluation is inadequate as it only compares against a single baseline. For a paper proposing a new framework, comparing with multiple state-of-the-art methods is essential to demonstrate the effectiveness and advantages of the proposed approach. The limited comparison significantly weakens the paper's experimental validation.

Overall, I find the paper poorly written, with issues including misuse of terminology, redundant expressions, unclear logical flow, and lack of focus. Moreover, the paper seems to compare against only one baseline, which makes the experimental results unconvincing to me.


[1] Agrahari, S. and Singh, A.K., 2022. Concept drift detection in data stream mining: A literature review. Journal of King Saud University-Computer and Information Sciences, 34(10), pp.9523-9540.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 1
Clarity: 1