Deep learning (DL) systems that perform image-level classification with convolutional neural networks (CNNs) have been shown to provide high-performance solutions for automated screening of eye diseases. Nevertheless, adversarial attacks have been recently screening settings, where there is restricted access to the systems and limited knowledge about certain factors, such as their CNN architecture or the data used for development.
Deep learning for automated screening of eye diseases.
We used the Kaggle dataset for diabetic retinopathy detection. It contains 88,702 manually-labelled color fundus images, which we split into test (12%) and development (88%). Development data were split into two equally-sized sets (d1 and d2); a third set (d3) was generated using half of the images in d2. In each development set, 80%/20% of the images were used for training/validation. All splits were done randomly at patient-level. As attacked system, we developed a randomly-initialized CNN based on the Inception-v3 architecture using d1. We performed the attacks (1) in a white-box (WB) setting, with full access to the attacked system to generate the adversarial images, and (2) in black-box (BB) settings, without access to the attacked system and using a surrogate system to craft the attacks. We simulated different BB settings, sequentially decreasing the available knowledge about the attacked system: same architecture, using d1 (BB-1); different architecture (randomly-initialized DenseNet-121), using d1 (BB-2); same architecture, using d2 (BB-3); different architecture, using d2 (BB-4); different architecture, using d3 (BB-5). In each setting, adversarial images containing non-perceptible noise were generated by applying the fast gradient sign method to each image of the test set and processed by the attacked system.
The performance of the attacked system to detect referable diabetic retinopathy without attacks and under the different attack settings was measured on the test set using the area under the receiver operating characteristic curve (AUC). Without attacks, the system achieved an AUC of 0.88. In each attack setting, the relative decrease in AUC with respect to the original performance was computed. In the WB setting, there was a 99.9% relative decrease in performance. In the BB-1 setting, the relative decrease in AUC was 67.3%. In the BB-2 setting, the AUC suffered a 40.2% relative decrease. In the BB-3 setting, the relative decrease was 37.9%. In the BB-4 setting, the relative decrease in AUC was 34.1%. Lastly, in the BB-5 setting, the performance of the attacked system decreased 3.8% regarding its original performance.
The results obtained in the different settings show a drastic decrease of the attacked DL system's vulnerability to adversarial attacks when the access and knowledge about it are limited. The impact on performance is extremely reduced when restricting the direct access to the system (from the WB to the BB-1 setting). The attacks become slightly less effective when not having access to the same development data (BB-3), compared to not using the same CNN architecture (BB-2). Attacks' effectiveness further decreases when both factors are unknown (BB-4). If the amount of development data is additionally reduced (BB-5), the original performance barely deteriorates. This last setting is the most similar to realistic screening settings, since most systems are currently closed source and use additional large private datasets for development. In conclusion, these factors should be acknowledged for future development of robust DL systems, as well as considered when evaluating the vulnerability of currently-available systems to adversarial attacks. Having limited access and knowledge about the systems determines the actual threat these attacks pose. We believe awareness about this matter will increase experts' trust and facilitate the integration of DL systems in real-world settings.