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<section class="publication-header">
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<h1 class="title is-1 publication-title">
Revisiting Adversarial Patches for Designing Camera-Agnostic Attacks against Person Detection
</h1>
</div>
</div>
</div>
</section>
<section class="publication-author-block">
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<span class="author-block">Anonymous Authors<sup></sup></span>
</div> -->
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://weihui1308.github.io/">Hui Wei</a><sup>1 *</sup>
<span class="author-block">
<a href="https://lightchaserx.github.io/">Zhixiang Wang</a><sup>2 *</sup>
<span class="author-block">
<a href="https://xiwen1.github.io/">Kewei Zhang</a><sup>1 *</sup>
<span class="author-block">
<a href="https://github.com/Hallucinatie">Jiaqi Hou</a><sup>1</sup>
<span class="author-block">
<a href="https://github.com/lywverdant">Yuanwei Liu</a><sup>1</sup>
<span class="author-block">
<a href="https://ha0tang.github.io/">Hao Tang</a><sup>3</sup>
<span class="author-block">
<a href="https://wangzwhu.github.io/home/">Zheng Wang</a><sup>1 †</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Wuhan University,</span>
<span class="author-block"><sup>2</sup>The University of Tokyo,</span>
<span class="author-block"><sup>3</sup>Peking University</span>
</div>
<br>
<div class="is-size-5 publication-authors">
<span class="author-block">Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)</span>
</div>
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<source src="./static/images/demo.mp4" type="video/mp4">
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<h2 class="subtitle has-text-centered">
<i>Video1</i> method shows the physical attack effect using visible light imaging.
</h2> -->
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<h2 class="subtitle has-text-centered">
Physical adversarial attacks of the <strong>baseline</strong> across <br> six different imaging devices.
</h2>
</div>
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<i>Video2</i> method shows the physical attack effect using thermal infrared imaging.
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<h2 class="subtitle has-text-centered">
Physical adversarial attacks of <strong>our method</strong> across <br> six different imaging devices.
</h2>
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<p>
Here, we present the comparison results between <strong>T-SEA</strong> [<i>Huang et al. 2023</i>] and <strong>our method</strong>. We control the scene to eliminate the influence of irrelevant factors. Click the arrow or drag to view more results.
</p>
</div>
</div>
</div>
</div>
</section>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Physical adversarial attacks can deceive deep neural networks (DNNs), leading to erroneous predictions in real-world scenarios. To uncover potential security risks, attacking the safety-critical task of person detection has garnered significant attention.
However, we observe that existing attack methods overlook the pivotal role of the camera in the physical adversarial attack workflow, involving capturing real-world scenes and converting them into digital images.
This oversight leads to instability and challenges in reproducing these attacks. In this work, we revisit patch-based attacks against person detectors and introduce a camera-agnostic physical adversarial attack to mitigate this limitation.
Specifically, we construct a differentiable camera Image Signal Processing (ISP) proxy network to compensate for physical-to-digital domain transition gap. Furthermore, the camera ISP proxy network serves as a defense module, forming an adversarial optimization framework with the attack module.
The attack module optimizes adversarial perturbations to maximize effectiveness, while the defense module optimizes the conditional parameters of the camera ISP proxy network to minimize attack effectiveness. These modules engage in an adversarial game, enhancing cross-camera stability.
Experimental results demonstrate that our proposed CAP (Camera-Agnostic Patch) attack effectively conceals persons from detectors across various imaging hardware, including two distinct cameras and four smartphones.
</p>
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<p>
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</div>
</div>
</div>
<h2 class="title is-3 has-text-centered">Pipeline</h2>
<img style="width: 100%;" src="./assets/display/pipeline_v6.png" alt="HyperNeRF architecture." />
<p>
Our pipeline comprises two mutually adversarial parts: Attacker and Defender.
The attacker optimizes adversarial perturbations to maximize attack effectiveness,
while the defender optimizes the conditional input hyperparameter of the ISP proxy network to minimize attack effectiveness.
The two parts cyclically alternate during the optimization stage.
</p>
</div>
</section>
<hr/>
<section class="section">
<div class="container is-max-desktop">
<div class="container is-max-desktop">
<div class="content has-text-justified">
<h2 class="title is-3">Camera-Agnostic Attacks</h2>
<p>
Compared to other methods that only succeed in attacking on individual imaging devices, our approach achieves stable attacks across all six imaging devices including two distinct cameras (Sony α7R4📷 and Canon DS126231📷)
and four smartphone cameras (iPhone15📱, RedmiK20📱, HuaweiP50📱, and SamsungS22📱).
The experiments are conducted using the YOLOv5 detector, with confidence threshold and NMS IOU threshold consistent with official settings, set at 0.25 and 0.45, respectively.
</p>
<div class="has-text-centered">
<img style="width: 100%;" src="./assets/display/physicalPersonresult.svg" alt="Person detection." />
</div>
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<h3 class="title is-4">Quantification of digital-space attacks</h3>
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<p>
Quantitative results of different attack methods under various ISP settings in digital space. Our CAP attack surpasses all existing methods in terms of attack success rate (ASR%).
The reason T-SEA performs well in Average Precision (AP%) but poorly in ASR is due to the multiple bounding box detections.
</p>
<div>
<img src="./assets/display/digitalTable.png" width="100%"/>
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<!-- <p>
can be represented as slices through this auxiliary shape
</p>
<div>
<img src="./assets/display/physicalPerson.png" width="100%"/>
</div>
<p>
We can naturally model topologically varying shapes by just moving the
slicing plane along the higher dimensions. For example, this animation was generated
by
moving the slicing plane from top to bottom:
</p> -->
</div>
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<h3 class="title is-4">Quantification of real-world attacks</h3>
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<p>
In all six sets of quantitative comparisons, our method achieves an ASR (Attack Success Rate) of over 90% across six imaging devices, demonstrating excellent attack effectiveness and stability on the person detection task.
</p>
<div>
<img src="./assets/display/physicalPerson.png" width="100%"/>
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</section>
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<section>
<!-- <h1 class="title is-3 has-text-centered">Results Showcase of Camera Lens Simulation
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<h2 class="title is-3">Results Showcase of our ISP Proxy Network
</h2>
<p>Here is an interactive viewer for the camera ISP proxy network of our defender. It generates the corresponding camera ISP-processed image based on conditional input parameters.
Drag the <span style="color: #29e">blue cursor</span> around to change the images on the right.</p>
<br /><br />
<div class="columns is-centered">
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X: Gamma Adjustment<br>
Y: Color Correction Matrix
<br />
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<div class="hyper-grid-wrapper">
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<img src="./static/figures/isp_gamma_cont_2.jpg" />
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Ground truth
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<div class="hyper-grid-wrapper">
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<img src="static/figures/449_gamma_cont_net.jpg" />
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Our ISP Proxy Network
</div>
</div>
<div class="info">
<span class="title is-5 has-text-left">
Gamma Adjustment:
</span>
<span class="is-text is-6">Gamma adjustment is a process that alters the luminance values of an image to match the characteristics of the display device. </span>
<br />
<span class="title is-5 has-text-left">
Color Correction Matrix:
</span>
<span class="is-text is-6">Color Correction Matrix is used to correct color inaccuracies in an image.</span>
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X: Brightness Contrast Control <br> Y: Non-Local Means
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<div class="hyper-grid-wrapper">
<div class="hyper-grid-rgb2">
<img src="./static/figures/isp_ccm_nlm_2.jpg" />
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Ground truth
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<div class="hyper-grid-wrapper">
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<img src="static/figures/449_ccm_nlm_net.jpg" />
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</div>
Our ISP Proxy Network
</div>
</div>
<div class="info">
<span class="title is-5 has-text-left">
Brightness Contrast Control:
</span>
<span class="is-text is6">Brightness Contrast Control controls the overall brightness and contrast of an image. </span>
<br />
<span class="title is-5 has-text-left">
Non-Local Means:
</span>
<span class="is-text is6">Non-Local Means is a denoising technique used to reduce noise in images while preserving details.</span>
</div>
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X: Spatial Filtering <br> Y: Hue Saturation Control
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<img src="./static/figures/isp_bnf_sat_2.jpg" />
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Ground truth
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<div class="hyper-grid-wrapper">
<div class="hyper-grid-rgb3">
<img src="static/figures/449_bnf_sat_net.jpg" />
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Our ISP Proxy Network
</div>
</div>
<div class="info">
<span class="title is-5 has-text-left">
Spatial Filtering:
</span>
<span class="is-text is-6">Spatial filtering is a technique used to enhance or suppress certain features within an image based on their spatial characteristics.</span>
<br />
<span class="title is-5 has-text-left">
Hue Saturation Control:
</span>
<span class="is-text is-6">Hue Saturation Control adjusts the hue and saturation of colors in an image. </span>
</div>
</section>
<hr />
<section class="section" id="BibTeX">
<div class="container content is-max-desktop">
<h2 class="title">BibTeX</h2>
<pre><code>
@inproceedings{wei2024cap,
title={Revisiting Adversarial Patches for Designing Camera-Agnostic Attacks against Person Detection},
author={Wei, Hui and Wang, Zhixiang and Zhang, Kewei and Hou, Jiaqi and Liu, Yuanwei and Tang, Hao and Wang, Zheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024}
}
</code></pre>
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