Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models

• URL: http://arxiv.org/abs/2409.06420v1
• Date: Tue, 10 Sep 2024 11:02:35 GMT
• Title: Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models
• Authors: Siyu Zhai, Zhibo He, Xiaofeng Cong, Junming Hou, Jie Gui, Jian Wei You, Xin Gong, James Tin-Yau Kwok, Yuan Yan Tang,
• Abstract summary: We make a first attempt to conduct adversarial attacks on five well-designed UWIE models on three common underwater image benchmark datasets. Results show that five models exhibit varying degrees of vulnerability to adversarial attacks and well-designed small perturbations on degraded images are capable of preventing UWIE models from generating enhanced results.
• Score: 16.272142350752805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based methods for underwater image enhancement (UWIE) have undergone extensive exploration. However, learning-based models are usually vulnerable to adversarial examples so as the UWIE models. To the best of our knowledge, there is no comprehensive study on the adversarial robustness of UWIE models, which indicates that UWIE models are potentially under the threat of adversarial attacks. In this paper, we propose a general adversarial attack protocol. We make a first attempt to conduct adversarial attacks on five well-designed UWIE models on three common underwater image benchmark datasets. Considering the scattering and absorption of light in the underwater environment, there exists a strong correlation between color correction and underwater image enhancement. On the basis of that, we also design two effective UWIE-oriented adversarial attack methods Pixel Attack and Color Shift Attack targeting different color spaces. The results show that five models exhibit varying degrees of vulnerability to adversarial attacks and well-designed small perturbations on degraded images are capable of preventing UWIE models from generating enhanced results. Further, we conduct adversarial training on these models and successfully mitigated the effectiveness of adversarial attacks. In summary, we reveal the adversarial vulnerability of UWIE models and propose a new evaluation dimension of UWIE models.

Related papers

• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• Dual Adversarial Resilience for Collaborating Robust Underwater Image Enhancement and Perception [54.672052775549]
  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.
  arXiv  Detail & Related papers  (2023-09-03T06:52:05Z)
• Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method [115.29382166356478]
  We introduce the adversarial retrieval attack (AREA) task. It is meant to trick DR models into retrieving a target document that is outside the initial set of candidate documents retrieved by the DR model. We find that the promising results that have previously been reported on attacking NRMs, do not generalize to DR models. We propose to formalize attacks on DR models as a contrastive learning problem in a multi-view representation space.
  arXiv  Detail & Related papers  (2023-08-19T00:24:59Z)
• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators [120.06891448820447]
  How to obtain clear and visually pleasant images has become a common concern of people. The task of underwater image enhancement (UIE) has also emerged as the times require. In this paper, we propose a physical model-guided GAN model for UIE, referred to as PUGAN. Our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics.
  arXiv  Detail & Related papers  (2023-06-15T07:41:12Z)
• AdvHaze: Adversarial Haze Attack [19.744435173861785]
  We introduce a novel adversarial attack method based on haze, which is a common phenomenon in real-world scenery. Our method can synthesize potentially adversarial haze into an image based on the atmospheric scattering model with high realisticity. We demonstrate that the proposed method achieves a high success rate, and holds better transferability across different classification models than the baselines.
  arXiv  Detail & Related papers  (2021-04-28T09:52:25Z)
• ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models [64.03398193325572]
  Inference attacks against Machine Learning (ML) models allow adversaries to learn about training data, model parameters, etc. We concentrate on four attacks - namely, membership inference, model inversion, attribute inference, and model stealing. Our analysis relies on a modular re-usable software, ML-Doctor, which enables ML model owners to assess the risks of deploying their models.
  arXiv  Detail & Related papers  (2021-02-04T11:35:13Z)
• Detection Defense Against Adversarial Attacks with Saliency Map [7.736844355705379]
  It is well established that neural networks are vulnerable to adversarial examples, which are almost imperceptible on human vision. Existing defenses are trend to harden the robustness of models against adversarial attacks. We propose a novel method combined with additional noises and utilize the inconsistency strategy to detect adversarial examples.
  arXiv  Detail & Related papers  (2020-09-06T13:57:17Z)
• Orthogonal Deep Models As Defense Against Black-Box Attacks [71.23669614195195]
  We study the inherent weakness of deep models in black-box settings where the attacker may develop the attack using a model similar to the targeted model. We introduce a novel gradient regularization scheme that encourages the internal representation of a deep model to be orthogonal to another. We verify the effectiveness of our technique on a variety of large-scale models.
  arXiv  Detail & Related papers  (2020-06-26T08:29:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.