Convolutional neural networks for image spam detection

Sharmin, Tazmina; Troia, Fabio Di; Potika, Katerina; Stamp, Mark

doi:10.1080/19393555.2020.1722867

Cited by 18 publications

(24 citation statements)

References 9 publications

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“…Despite the good results, Ref. [29] stated that the features used by these previous works were computationally extensive to extract. The authors of [29] proposed the use of the Canny edge detector combined with the raw image as features of images, which were then fed to a CNN model.…”

Section: Image-based Feature Extraction Techniquesmentioning

confidence: 99%

“…[29] stated that the features used by these previous works were computationally extensive to extract. The authors of [29] proposed the use of the Canny edge detector combined with the raw image as features of images, which were then fed to a CNN model. Their experiment showed good results on the ISH dataset compared to improved Challenge Dataset 1 and 2.…”

Section: Image-based Feature Extraction Techniquesmentioning

confidence: 99%

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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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Section: Image-based Feature Extraction Techniquesmentioning

confidence: 99%

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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“…Their proposed model achieved high accuracy values on text and hybrid (image/text) spam, with relatively low performance when only spam images were tested. Sharmin et al [11] presented image spam classifiers using SVM, multilayer perceptron (MLP) and CNN. For SVM and MLP, canny edge detector was used to extract efficient edge information.…”

Section: Literature Reviewmentioning

confidence: 99%

Weighted k-Nearest Neighbour for Image Spam Classification

Salih

Dhannoon

2021

eijs

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E-mail is an efficient and reliable data exchange service. Spams are undesired e-mail messages which are randomly sent in bulk usually for commercial aims. Obfuscated image spamming is one of the new tricks to bypass text-based and Optical Character Recognition (OCR)-based spam filters. Image spam detection based on image visual features has the advantage of efficiency in terms of reducing the computational cost and improving the performance. In this paper, an image spam detection schema is presented. Suitable image processing techniques were used to capture the image features that can differentiate spam images from non-spam ones. Weighted k-nearest neighbor, which is a simple, yet powerful, machine learning algorithm, was used as a classifier. The results confirm the effectiveness of the proposed schema as it is evaluated over two datasets. The first dataset is a real and benchmark dataset while the other is a real-like, modern, and more challenging dataset collected from social media and many public available image spam datasets. The obtained accuracy was 99.36% and 91% on benchmark and the proposed dataset, respectively.

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“…The identified gap in this authors work is that it did not capture how effective the model is when it comes to image-based spam, however, it is pertinent to bear in mind the two phenomena raised -the issue of "concept drift" and "catastrophic forgetting" so a model developed should be capable of detecting both and attempt to recycle an old spamming approach or detect the evolution of a new spamming approach. Sharmin, Di Troia, Potika, & Stamp, (2020) studied the problem of image spam detection, based on image analysis, where they applied convolutional neural networks (CNN). In the study, comparisons were made between results of other machine learning techniques and that of the study, and the results of previous related work.…”

Section: *Corresponding Authormentioning

confidence: 99%

“…The exponential growth and popularity of use, coupled with very high reachability, and a significantly low cost of operation, has made the email a more economical messaging platform for sending a new type of email called spam (Shandilya, Polash & Shiva 2014). Spam emails also called junk mails, are unsolicited bulk e-mails, sent to random recipients in large quantities, with commercial, fraudulent, or malicious intentions (Khawandi, Abdallah, & Ismail, 2019;Sharmin, Di Troia, Potika, & Stamp, 2020). Spammers (senders of spam emails) have evolved several spamming techniques to fool existing spam filters with identifiable weaknesses.…”

Section: Introductionmentioning

confidence: 99%

Development of a Machine Learning Model for Image-based Email Spam Detection

Onova¹,

Omotehinwa²

2021

FUOYEJET

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Combatting email spam has remained a very daunting task. Despite the over 99% accuracy in most non-image-based spam email detection, studies on image-based spam hardly attain such a high level of accuracy as new email spamming techniques that defeat existing spam filters emerges from time to time. The number of email spams sent out daily has remained a key factor in the continued use of spam. In this paper, a simple convolutional neural network model, 123DNet was developed and trained with 28,929 images drawn from 2 public datasets and a Personally Generated dataset. The model was optimized to the least set of layers to have 1 input layer, 2 embedded Convolutional layers as a hidden layer, and 3 neural network layers. The model was tested with a total of 4,339 images of the three dataset samples and then with a separate set of 1,200 images to test performance on never-seen-before images. A Classification Performance analysis was carried out using the confusion matrix. Performance metrics including Accuracy, Precision, True Negative Accuracy, Sensitivity, Specificity, and F1 Measure were computed to ascertain the model’s performance. The Model returned an F1 Score of 97% on a public dataset’s test sample and 88% on Never-seen-before test samples outperforming some pre-existing models while performing significantly well on the newly generated image test samples. It is recommended that a model that performed so well with new never-seen-before spam images be integrated into spam filtering systems. Keywords- Convolutional Neural Network, Deep Learning, Image-based Spam Detection

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Convolutional neural networks for image spam detection

Cited by 18 publications

References 9 publications

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

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

Weighted k-Nearest Neighbour for Image Spam Classification

Development of a Machine Learning Model for Image-based Email Spam Detection

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