Spam detection
35 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Spam detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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ViSpamReviews
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).
Cost-Aware Robust Tree Ensembles for Security Applications
There are various costs for attackers to manipulate the features of security classifiers.
Weight Poisoning Attacks on Pre-trained Models
neulab/RIPPLe
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We show that by applying a regularization method, which we call RIPPLe, and an initialization procedure, which we call Embedding Surgery, such attacks are possible even with limited knowledge of the dataset and fine-tuning procedure.
Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text
Mai-CS/enhanced-deep-convolutional-forest
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The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords.
Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks
joaopauloschuler/k-neural-api
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Mendel 2022
In Deep Convolutional Neural Networks (DCNNs), the parameter count in pointwise convolutions quickly grows due to the multiplication of the filters and input channels from the preceding layer.
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
graph-com/gad-nr
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Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains.
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.
