# Object-fabrication Targeted Attack for Object Detection

arXiv: https://arxiv.org/abs/2212.06431 · HTML: https://mlsanity.com/paper/2212.06431/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 54 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## Builds on (most cited first, 28 of 28)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [Adversarial Examples for Semantic Segmentation and Object Detection](https://mlsanity.com/paper/1703.08603/) (2017)
- [Object Detectors Emerge in Deep Scene CNNs](https://mlsanity.com/paper/1412.6856/) (2014)
- [Physically Realizable Adversarial Examples for LiDAR Object Detection](https://mlsanity.com/paper/2004.00543/) (2020)
- [Feature Importance-aware Transferable Adversarial Attacks](https://mlsanity.com/paper/2107.14185/) (2021)
- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Transferable Adversarial Attacks for Image and Video Object Detection](https://mlsanity.com/paper/1811.12641/) (2018)
- [Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition](https://mlsanity.com/paper/2105.03162/) (2021)
- [Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures](https://mlsanity.com/paper/2006.16974/) (2020)
- [Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations](https://mlsanity.com/paper/1801.08092/) (2018)
- [Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations](https://mlsanity.com/paper/2007.06189/) (2020)
- [Adv-watermark: A Novel Watermark Perturbation for Adversarial Examples](https://mlsanity.com/paper/2008.01919/) (2020)
- [Adversarial Examples that Fool Detectors](https://mlsanity.com/paper/1712.02494/) (2017)
- [IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking](https://mlsanity.com/paper/2103.14938/) (2021)
- [Feature Space Targeted Attacks by Statistic Alignment](https://mlsanity.com/paper/2105.11645/) (2021)
- [Context-Aware Transfer Attacks for Object Detection](https://mlsanity.com/paper/2112.03223/) (2021)
- [Deep Learning-based Forgery Attack on Document Images](https://mlsanity.com/paper/2102.00653/) (2021)
- [Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection](https://mlsanity.com/paper/2006.03184/) (2020)
- [Towards Visual Distortion in Black-Box Attacks](https://mlsanity.com/paper/2007.10593/) (2020)
- [Category-wise Attack: Transferable Adversarial Examples for Anchor Free Object Detection](https://mlsanity.com/paper/2003.04367/) (2020)

## Abstract (arXiv)

Recent studies have demonstrated that object detection networks are usually vulnerable to adversarial examples. Generally, adversarial attacks for object detection can be categorized into targeted and untargeted attacks. Compared with untargeted attacks, targeted attacks present greater challenges and all existing targeted attack methods launch the attack by misleading detectors to mislabel the detected object as a specific wrong label. However, since these methods must depend on the presence of the detected objects within the victim image, they suffer from limitations in attack scenarios and attack success rates. In this paper, we propose a targeted feature space attack method that can mislead detectors to `fabricate' extra designated objects regardless of whether the victim image contains objects or not. Specifically, we introduce a guided image to extract coarse-grained features of the target objects and design an innovative dual attention mechanism to filter out the critical features of the target objects efficiently. The attack performance of the proposed method is evaluated on MS COCO and BDD100K datasets with FasterRCNN and YOLOv5. Evaluation results indicate that the proposed targeted feature space attack method shows significant improvements in terms of image-specific, universality, and generalization attack performance, compared with the previous targeted attack for object detection.