  Deep Neural Networks (DNNs) are becoming widespread, particularly in
safety-critical areas. One prominent application is image recognition in
autonomous driving, where the correct classification of objects, such as
traffic signs, is essential for safe driving. Unfortunately, DNNs are prone to
backdoors, meaning that they concentrate on attributes of the image that should
be irrelevant for their correct classification. Backdoors are integrated into a
DNN during training, either with malicious intent (such as a manipulated
training process, because of which a yellow sticker always leads to a traffic
sign being recognised as a stop sign) or unintentional (such as a rural
background leading to any traffic sign being recognised as animal crossing,
because of biased training data).
  In this paper, we introduce AGNES, a tool to detect backdoors in DNNs for
image recognition. We discuss the principle approach on which AGNES is based.
Afterwards, we show that our tool performs better than many state-of-the-art
methods for multiple relevant case studies.
