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Article
Peer-Review Record

Adversarial Patch Attack on Multi-Scale Object Detection for UAV Remote Sensing Images

Remote Sens. 2022, 14(21), 5298; https://doi.org/10.3390/rs14215298
by Yichuang Zhang 1, Yu Zhang 1, Jiahao Qi 1, Kangcheng Bin 1, Hao Wen 1, Xunqian Tong 2 and Ping Zhong 1,*
Reviewer 1:
Reviewer 2: Anonymous
Reviewer 3:
Remote Sens. 2022, 14(21), 5298; https://doi.org/10.3390/rs14215298
Submission received: 9 September 2022 / Revised: 15 October 2022 / Accepted: 17 October 2022 / Published: 23 October 2022

Round 1

Reviewer 1 Report

Please find the attached report 

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

This paper proposed an adversarial attack method on object detection for RS data. Here are some issues:

1. The contribution in Introduction section should be rearranged. the proposed innovation is not reflected.

2. In P3L85, minimizing detect loss seems to be wrong.

3. Please explain in detail the difference between the proposed method and Ref 38, 39. Besides, the comparative experiments with Ref 39 should be added.

4. In Method section, I suggest first introducing the flowchart of the proposed method.

5. In Method section, I do not find an introduction on how to generate adversarial patches, please explain.

6. In Figure 5 and Figure 6. the rotation angle of adversarial random patches are different with other methods, and different methods should have the same rotation angle.

7. In Figure 7 and Figure 8, please give some examples of adversarial patch with rotation angle.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

The authors introduce the adversarial patch into the Yolo-V3 and Yolo-V5 to detect objects in remote sensing images. Several comments are given as follows:

1. Several parameters shown in the equations are not described—for example, Ci head.

2. Eq. 8  Lobj is supposed to be revised.

3. The adversarial patches with different methods are different. Could authors give more comparisons?

4. The results shown in Fig. 7 and 8 do demonstrate the feasibility of the proposed approach incorporating YOLO-V3 and YOLO-V5. Could the authors apply those methods to the same image?

5. The attack success rates (ASR) with thresholds seem to get more precise with a larger threshold. Did those defined ASRs reflect the correctness of object identification?

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

 

please see the attached file

 

Comments for author File: Comments.docx

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

1. According to the author's explanation, the adversarial patch is randomly initialized. Please reflect it in Figure 2.

2.I do not find the comparative experiments with Ref 39 (Ref 42) in the new version.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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