Spam detection
23 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Spam detection
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SMS Spam Collection Data Set
Traditional and Context-specific Spam Twitter
Most implemented papers
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios.
Evading classifiers in discrete domains with provable optimality guarantees
We introduce a graphical framework that (1) generalizes existing attacks in discrete domains, (2) can accommodate complex cost functions beyond $p$-norms, including financial cost incurred when attacking a classifier, and (3) efficiently produces valid adversarial examples with guarantees of minimal adversarial cost.
Stronger Data Poisoning Attacks Break Data Sanitization Defenses
kohpangwei/data-poisoning-journal-release
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In this paper, we develop three attacks that can bypass a broad range of common data sanitization defenses, including anomaly detectors based on nearest neighbors, training loss, and singular-value decomposition.
GANs for Semi-Supervised Opinion Spam Detection
Online reviews have become a vital source of information in purchasing a service (product).
An Automated Text Categorization Framework based on Hyperparameter Optimization
The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution.
Natural Language Processing: State of The Art, Current Trends and Challenges
anuragreddygv323/Important-stuff
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Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally.
EMFET: E-mail Features Extraction Tool
EMFET is an open source and flexible tool that can be used to extract a large number of features from any email corpus with emails saved in EML format.
Non-Negative Networks Against Adversarial Attacks
endgameinc/malware_evasion_competition
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Adversarial attacks against neural networks are a problem of considerable importance, for which effective defenses are not yet readily available.
DeepImageSpam: Deep Learning based Image Spam Detection
vinayakumarr/maching-learning-CDAC-Technopark
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Hackers and spammers are employing innovative and novel techniques to deceive novice and even knowledgeable internet users.
Spotting Collective Behaviour of Online Frauds in Customer Reviews
Online reviews play a crucial role in deciding the quality before purchasing any product.
