Deep Convolutional Neural Networks for Image Spam Classification

S. Sriram , R. Vinayakumar , V. Sowmya , Moez Krichen (1) , Dhouha Ben Noureddine , A. Shashank , K.P. Soman

1 REDCAD - Unité de Recherche en développement et contrôle d'applications distribuées

With the tremendous growth of the internet, cy-berspace is facing several threats from the attackers. Threats like spam emails account for 55% of total emails according to the Symantec monthly threat report. Over time, the attackers moved on to image spam to evade the text-based spam filters. To deal with this, the researchers have several machine learning and deep learning approaches that use various features like metadata, color, shape, texture features. But the Deep Convolutional Neural Network (DCNN) and transfer learning-based pre-trained CNN models are not explored much for Image spam classification. Therefore, in this work, 2 DCNN models along with few pre-trained ImageNet architectures like VGG19, Xception are trained on 3 different datasets. The effect of employing a Cost-sensitive learning approach to handle data imbalance is also studied. Some of the proposed models in this work achieves an accuracy up to 99% with zero false positive rate in best case.

Mots clés: Index Terms-Image spam; Deep learning; Convolutional neural network; Transfer learning; Cost-sensitive learning; Cyber security

Domaines: Cryptographie et sécurité [cs.CR]

S. Sriram, R. Vinayakumar, V. Sowmya, Moez Krichen, Dhouha Ben Noureddine, et al.. Deep Convolutional Neural Networks for Image Spam Classification. 2020. ⟨hal-02510594⟩