Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks

• URL: http://arxiv.org/abs/2403.10076v1
• Date: Fri, 15 Mar 2024 07:43:42 GMT
• Title: Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks
• Authors: Chong Wang, Yi Yu, Lanqing Guo, Bihan Wen,
• Abstract summary: Shadow removal is a task aimed at erasing regional shadows present in images and reinstating visually pleasing natural scenes with consistent illumination. Recent deep learning techniques have demonstrated impressive performance in image shadow removal, but their robustness against adversarial attacks remains largely unexplored. We propose a novel approach, called shadow-adaptive adversarial attack. Different from standard adversarial attacks, our attack budget is adjusted based on the pixel intensity in different regions of shadow images.
• Score: 30.071670081122203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shadow removal is a task aimed at erasing regional shadows present in images and reinstating visually pleasing natural scenes with consistent illumination. While recent deep learning techniques have demonstrated impressive performance in image shadow removal, their robustness against adversarial attacks remains largely unexplored. Furthermore, many existing attack frameworks typically allocate a uniform budget for perturbations across the entire input image, which may not be suitable for attacking shadow images. This is primarily due to the unique characteristic of spatially varying illumination within shadow images. In this paper, we propose a novel approach, called shadow-adaptive adversarial attack. Different from standard adversarial attacks, our attack budget is adjusted based on the pixel intensity in different regions of shadow images. Consequently, the optimized adversarial noise in the shadowed regions becomes visually less perceptible while permitting a greater tolerance for perturbations in non-shadow regions. The proposed shadow-adaptive attacks naturally align with the varying illumination distribution in shadow images, resulting in perturbations that are less conspicuous. Building on this, we conduct a comprehensive empirical evaluation of existing shadow removal methods, subjecting them to various levels of attack on publicly available datasets.

Related papers

• Regional Attention for Shadow Removal [10.575174563308046]
  This work devises a lightweight yet accurate shadow removal framework. We analyze the characteristics of the shadow removal task and design a novel regional attention mechanism. Unlike existing attention-based models, our regional attention strategy allows each shadow region to interact more rationally with its surrounding non-shadow areas.
  arXiv  Detail & Related papers  (2024-11-21T15:10:44Z)
• OmniSR: Shadow Removal under Direct and Indirect Lighting [16.90413085184936]
  A significant challenge in removing shadows from indirect illumination is obtaining shadow-free images to train the shadow removal network. We propose a novel rendering pipeline for generating shadowed and shadow-free images under direct and indirect illumination. We also propose an innovative shadow removal network that explicitly integrates semantic and geometric priors through concatenation and attention mechanisms.
  arXiv  Detail & Related papers  (2024-10-02T16:30:10Z)
• Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal [2.999888908665659]
  This study proposes a novel deep learning architecture, named Soft-Hard Attention U-net (SHAU), focusing on multiscale shadow removal. It provides a novel synthetic dataset, named Multiscale Shadow Removal dataset (MSRD), containing complex shadow patterns of multiple scales. The results demonstrate the effectiveness of SHAU over the relevant state-of-the-art shadow removal methods across various benchmark datasets.
  arXiv  Detail & Related papers  (2024-08-07T12:42:06Z)
• Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey [78.84004293081631]
  The patterns of shadows are arbitrary, varied, and often have highly complex trace structures. The degradation caused by shadows is spatially non-uniform, resulting in inconsistencies in illumination and color between shadow and non-shadow areas. Recent developments in this field are primarily driven by deep learning-based solutions.
  arXiv  Detail & Related papers  (2024-07-11T20:58:38Z)
• Cross-Modal Spherical Aggregation for Weakly Supervised Remote Sensing Shadow Removal [22.4845448174729]
  We propose a weakly supervised shadow removal network with a spherical feature space, dubbed S2-ShadowNet, to explore the best of both worlds for visible and infrared modalities. Specifically, we employ a modal translation (visible-to-infrared) model to learn the cross-domain mapping, thus generating realistic infrared samples. We contribute a large-scale weakly supervised shadow removal benchmark, including 4000 shadow images with corresponding shadow masks.
  arXiv  Detail & Related papers  (2024-06-25T11:14:09Z)
• SIRe-IR: Inverse Rendering for BRDF Reconstruction with Shadow and Illumination Removal in High-Illuminance Scenes [51.50157919750782]
  We present SIRe-IR, an implicit neural rendering inverse approach that decomposes the scene into environment map, albedo, and roughness. By accurately modeling the indirect radiance field, normal, visibility, and direct light simultaneously, we are able to remove both shadows and indirect illumination. Even in the presence of intense illumination, our method recovers high-quality albedo and roughness with no shadow interference.
  arXiv  Detail & Related papers  (2023-10-19T10:44:23Z)
• Content-based Unrestricted Adversarial Attack [53.181920529225906]
  We propose a novel unrestricted attack framework called Content-based Unrestricted Adversarial Attack. By leveraging a low-dimensional manifold that represents natural images, we map the images onto the manifold and optimize them along its adversarial direction.
  arXiv  Detail & Related papers  (2023-05-18T02:57:43Z)
• Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning [66.36104525390316]
  We propose a two-stage learning method, including reflectance guidance and a Shadow/Specular-Aware (S-Aware) network to tackle the problem. In the first stage, an initial reflectance layer free from shadows and specularities is obtained with the constraint of novel losses. To further enforce the reflectance layer to be independent of shadows and specularities in the second-stage refinement, we introduce an S-Aware network that distinguishes the reflectance image from the input image.
  arXiv  Detail & Related papers  (2022-11-27T07:26:41Z)
• Shadow-Aware Dynamic Convolution for Shadow Removal [80.82708225269684]
  We introduce a novel Shadow-Aware Dynamic Convolution (SADC) module to decouple the interdependence between the shadow region and the non-shadow region. Inspired by the fact that the color mapping of the non-shadow region is easier to learn, our SADC processes the non-shadow region with a lightweight convolution module. We develop a novel intra-convolution distillation loss to strengthen the information flow from the non-shadow region to the shadow region.
  arXiv  Detail & Related papers  (2022-05-10T14:00:48Z)
• Towards High Fidelity Face Relighting with Realistic Shadows [21.09340135707926]
  Our method learns to predict the ratio (quotient) image between a source image and the target image with the desired lighting. During training, our model also learns to accurately modify shadows by using estimated shadow masks. We demonstrate that our proposed method faithfully maintains the local facial details of the subject and can accurately handle hard shadows.
  arXiv  Detail & Related papers  (2021-04-02T00:28:40Z)
• Self-Supervised Shadow Removal [130.6657167667636]
  We propose an unsupervised single image shadow removal solution via self-supervised learning by using a conditioned mask. In contrast to existing literature, we do not require paired shadowed and shadow-free images, instead we rely on self-supervision and jointly learn deep models to remove and add shadows to images.
  arXiv  Detail & Related papers  (2020-10-22T11:33:41Z)

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.