Robust Scareware Image Detection

Christian Seifert, Jack W. Stokes, Christina Colcernian, John C. Platt, and Long Lu


In this paper, we propose an image-based detection method to identify web-based scareware attacks that is robust to evasion techniques. We evaluate the method on a large-scale data set that resulted in an equal error rate of 0.018%. Conceptually, false positives may occur when a visual element, such as a red shield, is embedded in a benign page. We suggest including additional orthogonal features or employing graders to mitigate this risk. A novel visualization technique is presented demonstrating the acquired classifier knowledge on a classified screenshot.


Publication typeInproceedings
Published inProceedings IEEE Conference on Acoustics, Speech, and Signal Processing
PublisherIEEE SPS
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