# Deep adversarial attack on target detection systems

arXiv: https://arxiv.org/abs/2108.05948 · HTML: https://mlsanity.com/paper/2108.05948/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 20 of 20)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [DenseBox: Unifying Landmark Localization with End to End Object Detection](https://mlsanity.com/paper/1509.04874/) (2015)
- [Boosting Adversarial Attacks with Momentum](https://mlsanity.com/paper/1710.06081/) (2017)
- [Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Prediction](https://mlsanity.com/paper/1803.08707/) (2018)
- [A Deep Face Identification Network Enhanced by Facial Attributes Prediction](https://mlsanity.com/paper/1805.00324/) (2018)
- [Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound](https://mlsanity.com/paper/1811.11979/) (2018)
- [Unsupervised Facial Geometry Learning for Sketch to Photo Synthesis](https://mlsanity.com/paper/1810.05361/) (2018)
- [Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval](https://mlsanity.com/paper/1902.04139/) (2019)
- [Restoring highly corrupted images by impulse noise using radial basis functions interpolation](https://mlsanity.com/paper/1607.06803/) (2016)
- [Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device](https://mlsanity.com/paper/1806.05363/) (2018)

## Built on by (most cited first, 2 of 2)

- [Physically Adversarial Infrared Patches with Learnable Shapes and Locations](https://mlsanity.com/paper/2303.13868/) (2023)
- [Real-world Adversarial Defense against Patch Attacks based on Diffusion Model](https://mlsanity.com/paper/2409.09406/) (2024)

## Abstract (arXiv)

Target detection systems identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. Deep Convolutional Neural Network (DCNN) classifiers have proven to be successful tools for computer vision applications. However,prior research confirms that even state of the art classifier models are susceptible to adversarial attacks. In this paper, we show how to generate adversarial infrared images by adding small perturbations to the targets region to deceive a DCNN-based target detector at remarkable levels. We demonstrate significant progress in developing visually imperceptible adversarial infrared images where the targets are visually recognizable by an expert but a DCNN-based target detector cannot detect the targets in the image.