Spam detection
31 papers with code • 1 benchmarks • 2 datasets
Most implemented papers
Non-Negative Networks Against Adversarial Attacks
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
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.
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation
The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global).
DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation
To show the feasibility of DeepCapture, we evaluate its performance with publicly available datasets consisting of 6, 000 spam and 2, 313 non-spam image samples.
Rank over Class: The Untapped Potential of Ranking in Natural Language Processing
Text classification has long been a staple within Natural Language Processing (NLP) with applications spanning across diverse areas such as sentiment analysis, recommender systems and spam detection.
Fact or Factitious? Contextualized Opinion Spam Detection
In this paper we perform an analytic comparison of a number of techniques used to detect fake and deceptive online reviews.
Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training
Online reviews are a vital source of information when purchasing a service or a product.
Adversarial Robustness with Non-uniform Perturbations
Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors.
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
Data exploration is an important step of every data science and machine learning project, including those involving textual data.