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Deep learning achieves outstanding results in many machine learning tasks.Nevertheless, it is vulnerable to backdoor attacks that modify the training setto embed a secret functionality in the trained model. The modified trainingsamples have a secret property, i. e., a trigger. At inference time, the secretfunctionality is activated when the input contains the trigger, while the modelfunctions correctly in other cases. While there are many known backdoor attacks(and defenses), deploying a stealthy attack is still far from trivial.Successfully creating backdoor triggers depends on numerous parameters.Unfortunately, research has not yet determined which parameters contribute mostto the attack performance.  This paper systematically analyzes the most relevant parameters for thebackdoor attacks, i.e., trigger size, position, color, and poisoning rate.Using transfer learning, which is very common in computer vision, we evaluatethe attack on state-of-the-art models (ResNet, VGG, AlexNet, and GoogLeNet) anddatasets (MNIST, CIFAR10, and TinyImageNet). Our attacks cover the majority ofbackdoor settings in research, providing concrete directions for future works.Our code is publicly available to facilitate the reproducibility of ourresults.

SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification