Deep convolutional neural network based image spam classification

Subramaniam, Sriram; Ravi, Vinayakumar; Sowmya, V.; Krichen, Moez; Noureddine, Dhouha Ben; Shashank, A

doi:10.36227/techrxiv.11999736

Cited by 7 publications

(8 citation statements)

References 15 publications

(26 reference statements)

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“…They proposed a FENOMAA (feature extraction neural network with OCR enhanced by mail authentication and analyzer of context) technique to filter spam mail. In general, researchers have analyzed spam images using CNN-based models, which have shown excellent performance in image analysis [35][36][37].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Features by Combining Visual and Text Information to Improve Spam Filtering Performance

et al. 2022

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The development of information and communication technology has created many positive outcomes, including convenience for people; however, cases of unsolicited communication, such as spam, also occur frequently. Spam is the indiscriminate transmission of unwanted information by anonymous users, called spammers. Spam content is indiscriminately transmitted to users in various forms, such as SMS, e-mail, and social network service posts, causing negative experiences for users of the service, while also creating costs, such as unnecessarily large amounts of network traffic. In addition, spam content includes phishing, hype or false advertising, and illegal content. Recently, spammers have also used images that contain stimulating content to effectively attract users’ curiosity and attention. Image spam contains more complex information than text, making it more difficult to analyze and to generalize its properties compared to text. Therefore, existing text-based spam detectors are vulnerable to spam image attacks, resulting in a decline in service quality. In this paper, a “hybrid features by combining visual and text information to improve spam filtering performance” method is proposed to reduce the occurrence of misclassification. The proposed method employs three sub-models to extract features from spam images and a classifier model to output the results using the features. Each sub-model extracts topic-, word-, and image-embedding-based features from spam images. In addition, the sub-models use optical character recognition, latent Dirichlet allocation, and word2Vec techniques to extract features from images. To evaluate spam image classification performance, the spam classifiers were trained using the extracted features and the results were measured using a confusion matrix. Our model achieved an accuracy of 0.9814 and a macro-F1 score of 0.9813. In addition, the application of OCR evasion techniques resulted in a decrease in recognition performance. Using the proposed model, a mean macro-F1 score of 0.9607 was obtained.

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Section: Related Workmentioning

confidence: 99%

Hybrid Features by Combining Visual and Text Information to Improve Spam Filtering Performance

et al. 2022

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“…Furthermore, Ref. [32] used the DCNN (deep convolutional neural network) and pretrained CNN for spam image detection. Three different datasets were used to prove the effectiveness of their proposed models.…”

Section: Image-based Feature Extraction Techniquesmentioning

confidence: 99%

MMPC-RF: A Deep Multimodal Feature-Level Fusion Architecture for Hybrid Spam E-mail Detection

et al. 2021

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Hybrid spam is an undesirable e-mail (electronic mail) that contains both image and text parts. It is more harmful and complex as compared to image-based and text-based spam e-mail. Thus, an efficient and intelligent approach is required to distinguish between spam and ham. To our knowledge, a small number of studies have been aimed at detecting hybrid spam e-mails. Most of these multimodal architectures adopted the decision-level fusion method, whereby the classification scores of each modality were concatenated and fed to another classification model to make a final decision. Unfortunately, this method not only demands many learning steps, but it also loses correlation in mixed feature space. In this paper, we propose a deep multimodal feature-level fusion architecture that concatenates two embedding vectors to have a strong representation of e-mails and increase the performance of the classification. The paragraph vector distributed bag of words (PV-DBOW) and the convolutional neural network (CNN) were used as feature extraction techniques for text and image parts, respectively, of the same e-mail. The extracted feature vectors were concatenated and fed to the random forest (RF) model to classify a hybrid e-mail as either spam or ham. The experiments were conducted on three hybrid datasets made using three publicly available corpora: Enron, Dredze, and TREC 2007. According to the obtained results, the proposed model provides a higher accuracy of 99.16% compared to recent state-of-the-art methods.

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“…Srinivasan et al [15] explored image spam detection based on deep features. The deep features are extracted using convolutional layers.…”

Section: Literature Reviewmentioning

confidence: 99%

Color Model Based Convolutional Neural Network for Image Spam Classification

Salih¹,

Dhannoon²

2020

ANJS

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For most of people, e-mail is the preferable medium for official communication. E-mail service providers face an endless challenge called spamming. Spammingis the exploitation of e-mail systems to send a bulk of unsolicited messages to a large number of recipients. Noisy image spamming is one of the new techniques to evade text analysis based and Optical Character Recognition (OCR) based spams filtering. In the present paper, Convolutional Neural Network (CNN) based on different color models was considered to address image spam problem. The proposed method was evaluated over a public image spam dataset. The results showed that the performance of the proposed CNN was affected by the color model used. The results also showed that XYZ model yields the best accuracy rate among all considered color models.

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Deep convolutional neural network based image spam classification

Cited by 7 publications

References 15 publications

Hybrid Features by Combining Visual and Text Information to Improve Spam Filtering Performance

Hybrid Features by Combining Visual and Text Information to Improve Spam Filtering Performance

MMPC-RF: A Deep Multimodal Feature-Level Fusion Architecture for Hybrid Spam E-mail Detection

Color Model Based Convolutional Neural Network for Image Spam Classification

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