Dual Adversarial Resilience for Collaborating Robust Underwater Image Enhancement and Perception

• URL: http://arxiv.org/abs/2309.01102v1
• Date: Sun, 3 Sep 2023 06:52:05 GMT
• Title: Dual Adversarial Resilience for Collaborating Robust Underwater Image Enhancement and Perception
• Authors: Zengxi Zhang, Zhiying Jiang, Zeru Shi, Jinyuan Liu, Risheng Liu
• Abstract summary: In this work, we introduce a collaborative adversarial resilience network, dubbed CARNet, for underwater image enhancement and subsequent detection tasks. We propose a synchronized attack training strategy with both visual-driven and perception-driven attacks enabling the network to discern and remove various types of attacks. Experiments demonstrate that the proposed method outputs visually appealing enhancement images and perform averagely 6.71% higher detection mAP than state-of-the-art methods.
• Score: 54.672052775549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the uneven scattering and absorption of different light wavelengths in aquatic environments, underwater images suffer from low visibility and clear color deviations. With the advancement of autonomous underwater vehicles, extensive research has been conducted on learning-based underwater enhancement algorithms. These works can generate visually pleasing enhanced images and mitigate the adverse effects of degraded images on subsequent perception tasks. However, learning-based methods are susceptible to the inherent fragility of adversarial attacks, causing significant disruption in results. In this work, we introduce a collaborative adversarial resilience network, dubbed CARNet, for underwater image enhancement and subsequent detection tasks. Concretely, we first introduce an invertible network with strong perturbation-perceptual abilities to isolate attacks from underwater images, preventing interference with image enhancement and perceptual tasks. Furthermore, we propose a synchronized attack training strategy with both visual-driven and perception-driven attacks enabling the network to discern and remove various types of attacks. Additionally, we incorporate an attack pattern discriminator to heighten the robustness of the network against different attacks. Extensive experiments demonstrate that the proposed method outputs visually appealing enhancement images and perform averagely 6.71% higher detection mAP than state-of-the-art methods.

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