Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection

• URL: http://arxiv.org/abs/2412.16358v2
• Date: Tue, 09 Sep 2025 07:12:30 GMT
• Title: Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection
• Authors: Mikael Yeghiazaryan, Sai Abhishek Siddhartha Namburu, Emily Kim, Stanislav Panev, Celso de Melo, Fernando De la Torre, Jessica K. Hodgins,
• Abstract summary: We propose realistic and practical constraints on texture resolution, limiting modified areas, and color ranges.<n>Our work analyzes the impact of shape modifications on attack performance.<n>We release both code and data to support adversarial attacks.
• Score: 47.373554501937264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting vehicles in aerial images is difficult due to complex backgrounds, small object sizes, shadows, and occlusions. Although recent deep learning advancements have improved object detection, these models remain susceptible to adversarial attacks (AAs), challenging their reliability. Traditional AA strategies often ignore practical implementation constraints. Our work proposes realistic and practical constraints on texture (lowering resolution, limiting modified areas, and color ranges) and analyzes the impact of shape modifications on attack performance. We conducted extensive experiments with three object detector architectures, demonstrating the performance-practicality trade-off: more practical modifications tend to be less effective, and vice versa. We release both code and data to support reproducibility at https://github.com/humansensinglab/texture-shape-adversarial-attacks.

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