CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception

TL;DR

This paper introduces CARNet, a novel adversarially resilient underwater image enhancement network that improves visual quality and detection accuracy by isolating and defending against adversarial attacks.

Contribution

The work proposes an invertible network, attack pattern discriminator, and bilevel attack optimization to enhance robustness against adversarial attacks in underwater image processing.

Findings

Outputs visually appealing enhanced images

Achieves 6.71% higher detection mAP than state-of-the-art methods

Demonstrates robustness against various adversarial attacks

Abstract

Due to the uneven 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 enhanced 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 visual quality enhancement and perceptual tasks. Furthermore, an attack pattern discriminator is introduced to adaptively identify and eliminate various types of attacks. Additionally, we propose a bilevel attack optimization strategy to heighten the robustness of the network against different types of attacks under the collaborative adversarial training of vision-driven and perception-driven attacks. Extensive experiments demonstrate that the proposed method outputs visually appealing enhancement images and performs an average 6.71% higher detection mAP than state-of-the-art methods.