396 research outputs found
Ranking News-Quality Multimedia
News editors need to find the photos that best illustrate a news piece and
fulfill news-media quality standards, while being pressed to also find the most
recent photos of live events. Recently, it became common to use social-media
content in the context of news media for its unique value in terms of immediacy
and quality. Consequently, the amount of images to be considered and filtered
through is now too much to be handled by a person. To aid the news editor in
this process, we propose a framework designed to deliver high-quality,
news-press type photos to the user. The framework, composed of two parts, is
based on a ranking algorithm tuned to rank professional media highly and a
visual SPAM detection module designed to filter-out low-quality media. The core
ranking algorithm is leveraged by aesthetic, social and deep-learning semantic
features. Evaluation showed that the proposed framework is effective at finding
high-quality photos (true-positive rate) achieving a retrieval MAP of 64.5% and
a classification precision of 70%.Comment: To appear in ICMR'1
Canary in Twitter Mine: Collecting Phishing Reports from Experts and Non-experts
The rise in phishing attacks via e-mail and short message service (SMS) has
not slowed down at all. The first thing we need to do to combat the
ever-increasing number of phishing attacks is to collect and characterize more
phishing cases that reach end users. Without understanding these
characteristics, anti-phishing countermeasures cannot evolve. In this study, we
propose an approach using Twitter as a new observation point to immediately
collect and characterize phishing cases via e-mail and SMS that evade
countermeasures and reach users. Specifically, we propose CrowdCanary, a system
capable of structurally and accurately extracting phishing information (e.g.,
URLs and domains) from tweets about phishing by users who have actually
discovered or encountered it. In our three months of live operation,
CrowdCanary identified 35,432 phishing URLs out of 38,935 phishing reports. We
confirmed that 31,960 (90.2%) of these phishing URLs were later detected by the
anti-virus engine, demonstrating that CrowdCanary is superior to existing
systems in both accuracy and volume of threat extraction. We also analyzed
users who shared phishing threats by utilizing the extracted phishing URLs and
categorized them into two distinct groups - namely, experts and non-experts. As
a result, we found that CrowdCanary could collect information that is
specifically included in non-expert reports, such as information shared only by
the company brand name in the tweet, information about phishing attacks that we
find only in the image of the tweet, and information about the landing page
before the redirect
Proposing a Scheme for Human Interactive Proof Test using Plasma Effect
Human Interactive Proofs (HIPs) are automatic inverse Turing tests, which are intended to differentiate between people and malicious computer programs. The mission of making good HIP system is a challenging issue, since the resultant HIP must be secure against attacks and in the same time it must be practical for humans. Text-based HIPs is one of the most popular HIPs types. It exploits the capability of humans to recite text images more than Optical Character Recognition (OCR), but the current text-based HIPs are not well-matched with rapid development of computer vision techniques, since they are either vey simply passed or very hard to resolve, thus this motivate that continuous efforts are required to improve the development of HIPs base text. In this paper, a new proposed scheme is designed for animated text-based HIP; this scheme exploits the gap between the usual perception of human and the ability of computer to mimic this perception and to achieve more secured and more human usable HIP. This scheme could prevent attacks since it's hard for the machine to distinguish characters with animation environment displayed by digital video, but it's certainly still easy and practical to be used by humans because humans are attuned to perceiving motion easily. The proposed scheme has been tested by many Optical Character Recognition applications, and it overtakes all these tests successfully and it achieves a high usability rate of 95%
Hybrid Machine Learning Algorithms for Email and Malware Spam Filtering: A Review
In this paper, we presented a review of the state-of-the-art hybrid machine learning algorithms that were being used for email effective computing. For this reason, three research questions were formed, and the questions were answered by studying and analyzing related papers collected from some well-established scientific databases (Springer Link, IEEE Explore, Web of Science, and Scopus) based on some exclusion and inclusion criteria. The result presented the common Hybrid ML algorithms used to enhance email spam filtering. Also, the state-of-the-art datasets used for email and malware spam filtering were presented. 
Image Spam Analysis
Image spam is unsolicited bulk email, where the message is embedded in an image. This technique is used to evade text-based spam lters. In this research, we analyze and compare two novel approaches for detecting spam images. Our rst approach focuses on the extraction of a broad set of image features and selection of an optimal subset using a Support Vector Machine (SVM). Our second approach is based on Principal Component Analysis (PCA), where we determine eigenvectors for a set of spam images and compute scores by projecting images onto the resulting eigenspace. Both approaches provide high accuracy with low computational complexity. Further, we develop a new spam image dataset that should prove valuable for improving image spam detection capabilities
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