@inproceedings{seifert2013robust,
author = {Seifert, Christian and Stokes, Jay and Colcernian, Christina and Platt, John and Lu, Long},
title = {Robust scareware image detection},
booktitle = {2013 International Conference on Acoustics, Speech, and Signal Processing},
year = {2013},
month = {May},
abstract = {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.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/robust-scareware-image-detection/},
pages = {2920-2924},
}