# Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection

arXiv: https://arxiv.org/abs/2011.06978 · HTML: https://mlsanity.com/paper/2011.06978/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [On the Adversarial Robustness of Vision Transformers](https://mlsanity.com/paper/2103.15670/) (2021)
- [Reveal of Vision Transformers Robustness against Adversarial Attacks](https://mlsanity.com/paper/2106.03734/) (2021)
- [Multi-Head Self-Attention via Vision Transformer for Zero-Shot Learning](https://mlsanity.com/paper/2108.00045/) (2021)
- [Implicit and Explicit Attention for Zero-Shot Learning](https://mlsanity.com/paper/2110.00860/) (2021)

## Abstract (arXiv)

Deep neural network approaches have demonstrated high performance in object recognition (CNN) and detection (Faster-RCNN) tasks, but experiments have shown that such architectures are vulnerable to adversarial attacks (FFF, UAP): low amplitude perturbations, barely perceptible by the human eye, can lead to a drastic reduction in labeling performance. This article proposes a new context module, called textitTransformer-Encoder Detector Module, that can be applied to an object detector to (i) improve the labeling of object instances; and (ii) improve the detector's robustness to adversarial attacks. The proposed model achieves higher mAP, F1 scores and AUC average score of up to 13% compared to the baseline Faster-RCNN detector, and an mAP score 8 points higher on images subjected to FFF or UAP attacks due to the inclusion of both contextual and visual features extracted from scene and encoded into the model. The result demonstrates that a simple ad-hoc context module can improve the reliability of object detectors significantly.