  Backdoor attack has emerged as a novel and concerning threat to AI security.
These attacks involve the training of Deep Neural Network (DNN) on datasets
that contain hidden trigger patterns. Although the poisoned model behaves
normally on benign samples, it exhibits abnormal behavior on samples containing
the trigger pattern. However, most existing backdoor attacks suffer from two
significant drawbacks: their trigger patterns are visible and easy to detect by
backdoor defense or even human inspection, and their injection process results
in the loss of natural sample features and trigger patterns, thereby reducing
the attack success rate and model accuracy. In this paper, we propose a novel
backdoor attack named SATBA that overcomes these limitations using spatial
attention and an U-net based model. The attack process begins by using spatial
attention to extract meaningful data features and generate trigger patterns
associated with clean images. Then, an U-shaped model is used to embed these
trigger patterns into the original data without causing noticeable feature
loss. We evaluate our attack on three prominent image classification DNN across
three standard datasets. The results demonstrate that SATBA achieves high
attack success rate while maintaining robustness against backdoor defenses.
Furthermore, we conduct extensive image similarity experiments to emphasize the
stealthiness of our attack strategy. Overall, SATBA presents a promising
approach to backdoor attack, addressing the shortcomings of previous methods
and showcasing its effectiveness in evading detection and maintaining high
attack success rate.
