  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 \textit{Transformer-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.
