Bias Field Poses a Threat to DNN-based X-Ray Recognition

The chest X-ray plays a key role in screening and diagnosis of many lung diseases including the COVID-19. More recently, many works construct deep neural networks (DNNs) for chest X-ray images to realize automated and efficient diagnosis of lung diseases. However, bias field caused by the improper medical image acquisition process widely exists in the chest X-ray images while the robustness of DNNs to the bias field is rarely explored, which definitely poses a threat to the X-ray-based automated diagnosis system. In this paper, we study this problem based on the recent adversarial attack and propose a brand new attack, i.e., the adversarial bias field attack where the bias field instead of the additive noise works as the adversarial perturbations for fooling the DNNs. This novel attack posts a key problem: how to locally tune the bias field to realize high attack success rate while maintaining its spatial smoothness to guarantee high realisticity. These two goals contradict each other and thus has made the attack significantly challenging. To overcome this challenge, we propose the adversarial-smooth bias field attack that can locally tune the bias field with joint smooth & adversarial constraints. As a result, the adversarial X-ray images can not only fool the DNNs effectively but also retain very high level of realisticity. We validate our method on real chest X-ray datasets with powerful DNNs, e.g., ResNet50, DenseNet121, and MobileNet, and show different properties to the state-of-the-art attacks in both image realisticity and attack transferability. Our method reveals the potential threat to the DNN-based X-ray automated diagnosis and can definitely benefit the development of bias-field-robust automated diagnosis system.

Adversarial Exposure Attack on Diabetic Retinopathy Imagery

Diabetic retinopathy (DR) is a leading cause of vision loss in the world and numerous cutting-edge works have built powerful deep neural networks (DNNs) to automatically classify the DR cases via the retinal fundus images (RFIs). However, RFIs are usually affected by the widely existing camera exposure while the robustness of DNNs to the exposure is rarely explored. In this paper, we study this problem from the viewpoint of adversarial attack and identify a totally new task, i.e., adversarial exposure attack generating adversarial images by tuning image exposure to mislead the DNNs with significantly high transferability. To this end, we first implement a straightforward method, i.e., multiplicative-perturbation-based exposure attack, and reveal the big challenges of this new task. Then, to make the adversarial image naturalness, we propose the adversarial bracketed exposure fusion that regards the exposure attack as an element-wise bracketed exposure fusion problem in the Laplacian-pyramid space. Moreover, to realize high transferability, we further propose the convolutional bracketed exposure fusion where the element-wise multiplicative operation is extended to the convolution. We validate our method on the real public DR dataset with the advanced DNNs, e.g., ResNet50, MobileNet, and EfficientNet, showing our method can achieve high image quality and success rate of the transfer attack. Our method reveals the potential threats to the DNN-based DR automated diagnosis and can definitely benefit the development of exposure-robust automated DR diagnosis method in the future.

Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack

Image denoising techniques have been widely employed in multimedia devices as an image post-processing operation that can remove sensor noise and produce visually clean images for further AI tasks, e.g., image classification. In this paper, we investigate a new task, adversarial denoise attack, that stealthily embeds attacks inside the image denoising module. Thus it can simultaneously denoise input images while fooling the state-of-the-art deep models. We formulate this new task as a kernel prediction problem and propose the adversarial-denoising kernel prediction that can produce adversarial-noiseless kernels for effective denoising and adversarial attacking simultaneously. Furthermore, we implement an adaptive perceptual region localization to identify semantic-related vulnerability regions with which the attack can be more effective while not doing too much harm to the denoising. Thus, our proposed method is termed as Pasadena (Perceptually Aware and Stealthy Adversarial DENoise Attack). We validate our method on the NeurIPS'17 adversarial competition dataset and demonstrate that our method not only realizes denoising but has advantages of high success rate and transferability over the state-of-the-art attacks.