Details

Autor(en) / Beteiligte

• Bortsova, Gerda
• González-Gonzalo, Cristina
• Wetstein, Suzanne C.
• Dubost, Florian
• Katramados, Ioannis
• Hogeweg, Laurens
• Liefers, Bart
• van Ginneken, Bram
• Pluim, Josien P.W.
• Veta, Mitko
• Sánchez, Clara I.
• de Bruijne, Marleen

Titel

Adversarial attack vulnerability of medical image analysis systems: Unexplored factors

Ist Teil von

• Medical image analysis, 2021-10, Vol.73, p.102141-102141, Article 102141

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2021

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •We study black-box adversarial attacks on deep learning in medical imaging.•We study the vulnerability of deep learning systems in three medical imaging domains.•ImageNet pre-training may substantially increase adversarial attack vulnerability.•Disjoint training data between target and attacker model decreases attack performance.•We give recommendations for system design and evaluation of adversarial robustness. [Display omitted] Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulnerable to adversarial attacks due to strong financial incentives and the associated technological infrastructure. In this paper, we study previously unexplored factors affecting adversarial attack vulnerability of deep learning MedIA systems in three medical domains: ophthalmology, radiology, and pathology. We focus on adversarial black-box settings, in which the attacker does not have full access to the target model and usually uses another model, commonly referred to as surrogate model, to craft adversarial examples that are then transferred to the target model. We consider this to be the most realistic scenario for MedIA systems. Firstly, we study the effect of weight initialization (pre-training on ImageNet or random initialization) on the transferability of adversarial attacks from the surrogate model to the target model, i.e., how effective attacks crafted using the surrogate model are on the target model. Secondly, we study the influence of differences in development (training and validation) data between target and surrogate models. We further study the interaction of weight initialization and data differences with differences in model architecture. All experiments were done with a perturbation degree tuned to ensure maximal transferability at minimal visual perceptibility of the attacks. Our experiments show that pre-training may dramatically increase the transferability of adversarial examples, even when the target and surrogate’s architectures are different: the larger the performance gain using pre-training, the larger the transferability. Differences in the development data between target and surrogate models considerably decrease the performance of the attack; this decrease is further amplified by difference in the model architecture. We believe these factors should be considered when developing security-critical MedIA systems planned to be deployed in clinical practice. We recommend avoiding using only standard components, such as pre-trained architectures and publicly available datasets, as well as disclosure of design specifications, in addition to using adversarial defense methods. When evaluating the vulnerability of MedIA systems to adversarial attacks, various attack scenarios and target-surrogate differences should be simulated to achieve realistic robustness estimates. The code and all trained models used in our experiments are publicly available.3