Information Processing for Size-Based Adversarial Patch Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting adversarial patch attacks in image AI for object detection, such as at self-checkouts and license plate recognition, fail to accurately identify and mitigate the impact of adversarial patches due to random size and position of added missingness, leading to overlooked fraud and misrecognition.
Innovation Solution
An information processing program that determines a missing rate based on the minimum size of an adversarial patch, generates an image with missingness exceeding this rate, and compares detection results between original and modified images to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If missingness with random size and position is added to the input image, then the image processing can be performed, but the missingness may fail to cover the adversarial patch and fail to reduce the effectiveness of the patch
Solution Approach 1:
The patent changes the parameters of missingness generation by determining a missing rate based on the minimum size of the adversarial patch and generating missingness that exceeds this rate. This ensures that the missingness is sufficiently large to cover the patch, transforming the random missingness into targeted missingness that reliably detects patch attacks.
2Ease of operation
If random missingness is added to detect patch attacks, then detection method can be implemented, but detection accuracy is insufficient due to failure to cover the patch
Solution Approach 1:
The patent determines a missing rate based on the minimum size of the adversarial patch and generates missingness exceeding this rate. This parameter change ensures that the missingness covers the patch effectively, significantly improving detection accuracy while maintaining ease of implementation through automated parameter calculation.
3Reliability
If missingness exceeding the missing rate is added to the first image, then the adversarial patch can be effectively covered, but the complexity of image processing increases
Solution Approach 1:
The patent calculates the missing rate based on the minimum size of the adversarial patch and generates missingness that exceeds this rate. By using a clear mathematical criterion (missing rate = f(minimum patch size)), the patent achieves reliable patch coverage while keeping the processing logic simple and automated, avoiding unnecessary complexity.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An information processing program causes a computer (10) to execute a process including acquiring a first value representing a size of a region of an object included in a first image, determining a missing rate for an adversarial patch, based on a second value representing a minimum size of the adversarial patch acquired according to the first value, generating a second image in which missingness exceeding the missing rate is added to the first image, and comparing a first detection result obtained by inputting the first image into an object detection model with a second detection result obtained by inputting the second image into the object detection model.