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Spam detection

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/CPSSD/LUCAS

공식 코드

Fact or Factitious? Contextualized Opinion Spam Detection

https://github.com/Giruvegan/stoneskipping

공식 코드

Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation

https://github.com/INGEOTEC/microTC

공식 코드

An Automated Text Categorization Framework based on Hyperparameter Optimization

https://github.com/LCS2-IIITD/DeFrauder

공식 코드

Spotting Collective Behaviour of Online Frauds in Customer Reviews

https://github.com/Mai-CS/Deep-Convolutional-Forest

공식 코드

Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text

https://github.com/Mai-CS/enhanced-deep-convolutional-forest

공식 코드

Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text

https://github.com/MateoLopez00/Imbalanced-Learning-Empirical-Evaluation

공식 코드

Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets

https://github.com/QData/deepWordBug

공식 코드

Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

https://github.com/Sargaleano/Madlib-Spam-Attack-BERT

공식 코드

Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam Detection

https://github.com/WadeaHijjawi/EmailFeaturesExtraction

공식 코드

EMFET: E-mail Features Extraction Tool

https://github.com/airesearchuwt/spamGAN

공식 코드

Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training

https://github.com/akanshaaga/reg_appnp

공식 코드

Addressing the Impact of Localized Training Data in Graph Neural Networks

https://github.com/amazon-research/adversarial-robustness-with-nonuniform-perturbations

공식 코드

Adversarial Robustness with Non-uniform Perturbations

https://github.com/atapour/rank-over-class

공식 코드

Rank over Class: The Untapped Potential of Ranking in Natural Language Processing

https://github.com/batsresearch/alfred

공식 코드

Alfred: A System for Prompted Weak Supervision

https://github.com/chasesecurity/spamdam

공식 코드

SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection

https://github.com/dbsheta/spam-detection-using-deep-learning/blob/master/main.py

공식 코드

DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation

https://github.com/graph-com/gad-nr

공식 코드

GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

https://github.com/jpmorganchase/llm-email-spam-detection

공식 코드

Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection

https://github.com/kohpangwei/data-poisoning-journal-release

공식 코드

Stronger Data Poisoning Attacks Break Data Sanitization Defenses

https://github.com/mmilkova/nlp-values

공식 코드

Detecting value-expressive text posts in Russian social media

https://github.com/nechbamohammed/swiftlogisticreg

공식 코드

High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation Comparison

https://github.com/neulab/RIPPLe

공식 코드

Weight Poisoning Attacks on Pre-trained Models

https://github.com/nlpgti/data_drift

공식 코드

Online detection and infographic explanation of spam reviews with data drift adaptation

https://github.com/pinterest/atg-research/tree/main/omnisearchsage

공식 코드

Modeling User Behavior With Interaction Networks for Spam Detection

https://github.com/plkumjorn/GrASP

공식 코드

GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns

https://github.com/saeidshirazi/Email-Spam-Detection-zeroshot

공식 코드

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models

https://github.com/spring-epfl/trickster

공식 코드

Evading classifiers in discrete domains with provable optimality guarantees

https://github.com/surrealyz/growtrees

공식 코드

Cost-Aware Robust Tree Ensembles for Security Applications

https://github.com/tedzhouhk/GCNP

공식 코드

Accelerating Large Scale Real-Time GNN Inference using Channel Pruning

https://github.com/wenliangdai/multi-task-offensive-language-detection

공식 코드

Text generation for dataset augmentation in security classification tasks

https://github.com/GU-DataLab/context-spam

https://github.com/Koukyosyumei/AIJack

Cost-Aware Robust Tree Ensembles for Security Applications

https://github.com/alankarj/robust_nlp

Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

https://github.com/anuragreddygv323/Important-stuff

Natural Language Processing: State of The Art, Current Trends and Challenges

https://github.com/bigheiniu/spamGAN

GANs for Semi-Supervised Opinion Spam Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.