77,231 research outputs found
DeltaPhish: Detecting Phishing Webpages in Compromised Websites
The large-scale deployment of modern phishing attacks relies on the automatic
exploitation of vulnerable websites in the wild, to maximize profit while
hindering attack traceability, detection and blacklisting. To the best of our
knowledge, this is the first work that specifically leverages this adversarial
behavior for detection purposes. We show that phishing webpages can be
accurately detected by highlighting HTML code and visual differences with
respect to other (legitimate) pages hosted within a compromised website. Our
system, named DeltaPhish, can be installed as part of a web application
firewall, to detect the presence of anomalous content on a website after
compromise, and eventually prevent access to it. DeltaPhish is also robust
against adversarial attempts in which the HTML code of the phishing page is
carefully manipulated to evade detection. We empirically evaluate it on more
than 5,500 webpages collected in the wild from compromised websites, showing
that it is capable of detecting more than 99% of phishing webpages, while only
misclassifying less than 1% of legitimate pages. We further show that the
detection rate remains higher than 70% even under very sophisticated attacks
carefully designed to evade our system.Comment: Preprint version of the work accepted at ESORICS 201
Complex Event Recognition from Images with Few Training Examples
We propose to leverage concept-level representations for complex event
recognition in photographs given limited training examples. We introduce a
novel framework to discover event concept attributes from the web and use that
to extract semantic features from images and classify them into social event
categories with few training examples. Discovered concepts include a variety of
objects, scenes, actions and event sub-types, leading to a discriminative and
compact representation for event images. Web images are obtained for each
discovered event concept and we use (pretrained) CNN features to train concept
classifiers. Extensive experiments on challenging event datasets demonstrate
that our proposed method outperforms several baselines using deep CNN features
directly in classifying images into events with limited training examples. We
also demonstrate that our method achieves the best overall accuracy on a
dataset with unseen event categories using a single training example.Comment: Accepted to Winter Applications of Computer Vision (WACV'17
Webly Supervised Learning of Convolutional Networks
We present an approach to utilize large amounts of web data for learning
CNNs. Specifically inspired by curriculum learning, we present a two-step
approach for CNN training. First, we use easy images to train an initial visual
representation. We then use this initial CNN and adapt it to harder, more
realistic images by leveraging the structure of data and categories. We
demonstrate that our two-stage CNN outperforms a fine-tuned CNN trained on
ImageNet on Pascal VOC 2012. We also demonstrate the strength of webly
supervised learning by localizing objects in web images and training a R-CNN
style detector. It achieves the best performance on VOC 2007 where no VOC
training data is used. Finally, we show our approach is quite robust to noise
and performs comparably even when we use image search results from March 2013
(pre-CNN image search era)
Detecting complex events in user-generated video using concept classifiers
Automatic detection of complex events in user-generated
videos (UGV) is a challenging task due to its new characteristics differing from broadcast video. In this work, we firstly summarize the new characteristics of UGV, and then explore how to utilize concept classifiers to recognize complex events in UGV content. The method starts from manually selecting a variety of relevant concepts, followed byconstructing classifiers for these concepts. Finally, complex event detectors are learned by using the concatenated probabilistic scores of these concept classifiers as features. Further, we also compare three different fusion operations of probabilistic scores, namely Maximum, Average and Minimum fusion. Experimental results suggest that our method provides promising results. It also shows that Maximum fusion tends to give better performance for most complex events
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