Spam detection

31 papers with code • 1 benchmarks • 2 datasets

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Most implemented papers

Non-Negative Networks Against Adversarial Attacks

endgameinc/malware_evasion_competition 15 Jun 2018

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 3 Oct 2018

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

LCS2-IIITD/DeFrauder 31 May 2019

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

Giruvegan/stoneskipping IJCNLP 2019

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

dbsheta/spam-detection-using-deep-learning 16 Jun 2020

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

atapour/rank-over-class 10 Sep 2020

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

CPSSD/LUCAS ACL 2019

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

airesearchuwt/spamGAN 24 Dec 2020

Online reviews are a vital source of information when purchasing a service or a product.

Adversarial Robustness with Non-uniform Perturbations

amazon-research/adversarial-robustness-with-nonuniform-perturbations NeurIPS 2021

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

plkumjorn/GrASP LREC 2022

Data exploration is an important step of every data science and machine learning project, including those involving textual data.