6533b86efe1ef96bd12cb453
RESEARCH PRODUCT
One-Pixel Attack Deceives Computer-Assisted Diagnosis of Cancer
Samir PuuskaJoni KorpihalkolaTero KokkonenTuomo Sipolasubject
FOS: Computer and information sciencesComputer Science - Machine LearningComputer Science - Cryptography and SecurityComputer scienceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONMachine Learning (cs.LG)Medical imagingComputer visionkonenäköIBMkyberturvallisuusPixelbusiness.industryPerspective (graphical)diagnostiikkakoneoppiminenDifferential evolutionWhole slide imageReversingsyöpätauditArtificial intelligencebusinessCryptography and Security (cs.CR)verkkohyökkäyksetdescription
Computer vision and machine learning can be used to automate various tasks in cancer diagnostic and detection. If an attacker can manipulate the automated processing, the results can be devastating and in the worst case lead to wrong diagnosis and treatment. In this research, the goal is to demonstrate the use of one-pixel attacks in a real-life scenario with a real pathology dataset, TUPAC16, which consists of digitized whole-slide images. We attack against the IBM CODAIT's MAX breast cancer detector using adversarial images. These adversarial examples are found using differential evolution to perform the one-pixel modification to the images in the dataset. The results indicate that a minor one-pixel modification of a whole slide image under analysis can affect the diagnosis by reversing the automatic diagnosis result. The attack poses a threat from the cyber security perspective: the one-pixel method can be used as an attack vector by a motivated attacker. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-12-01 |