Image spam filtering using convolutional neural networks

Fan, Aiwan; Yang, Zhen

doi:10.1007/s00779-018-1168-8

Cited by 21 publications

(14 citation statements)

References 15 publications

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“…ML-based approaches require manual feature engineering which can be averted by using Deep Learning (DL) techniques. In [17], the performance of several CNN based models is studied for image spam recognition. CNN models like VGG, Spatial Pyramid Pooling (SPP) network, Weighted Spatial Pyramid (WSP) network, etc.…”

Section: Related Workmentioning

confidence: 99%

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Deep convolutional neural network based image spam classification

Subramaniam¹,

Ravi²,

Sowmya³

et al. 2020

Preprint

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With the tremendous growth of the internet, cyberspace 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.

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Section: Related Workmentioning

confidence: 99%

“…WSP network performed better than other models in terms of accuracy. Similar to [17], in [18], SPP Net is used for image spam detection. In [19], CNN based models are used for Instagram image spam detection.…”

Section: Related Workmentioning

confidence: 99%

Deep convolutional neural network based image spam classification

Subramaniam¹,

Ravi²,

Sowmya³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Additionally, the definition of inappropriate content is varied in the literature. Some studies describe inappropriate content as spam content or not-safe-for-work content as in [6][7] [8], where other studies focused on a specific type of content, such as text messages, and performed analysis processes to detect inappropriate content such as abusive language [9] or bullying behavior [10]. Spam URLs have also been investigated in [11] using behavioral analysis.…”

Section: A Spam Detectionmentioning

confidence: 99%

SentiFilter: A Personalized Filtering Model for Arabic Semi-Spam Content based on Sentimental and Behavioral Analysis

Alsulami¹,

Al-Aama²

2020

IJACSA

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Unwanted content in online social network services is a substantial issue that is continuously growing and negatively affecting the user-browsing experience. Current practices do not provide personalized solutions that meet each individual's needs and preferences. Therefore, there is a potential demand to provide each user with a personalized level of protection against what he/she perceives as unwanted content. Thus, this paper proposes a personalized filtering model, which we named SentiFilter. It is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics. An experiment involving 80,098 Twitter messages from 32 users was conducted to evaluate the effectiveness of the SentiFilter model. The effectiveness was measured in terms of the consistency between the implicit feedback derived from the SentiFilter model towards five selected topics and the explicit feedback collected explicitly from participants towards the same topics. Results reveal that commenting behavior is more effective than liking behavior to detect unwanted content because of its high consistency with users' explicit feedback. Findings also indicate that sentiment of users' comments does not reflect users' perception of unwanted content. The results of implicit feedback derived from the SentiFilter model accurately agree with users' explicit feedback by the indication of the low statistical significance difference between the two sets. The proposed model is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.

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“…Next, we discuss the use of Artificial Neural Networks in unsolicited visual data classification. We provide the In [13], the author uses an optimized approach for image spam filtering. They use data augmentation to improve the quality and quantity of the data sample.…”

Section: Fig 2 Optical Character Recognition Sequencementioning

confidence: 99%

Image Spam Filtering using Machine Learning Techniques

2019

ijrte

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Unsolicited visual data is undesirable in any form. The art of hiding malicious content in images and adding them as attachments to electronic mails has become a popular nuisance. In recent years, attackers have developed various new techniques to evade traditional spam classification systems. Text-based spam classification has been in focus for a long time and, researchers have successfully created a prodigal system for identifying spam text in electronic mails using Optical Character Recognition technology. In the last decade, extensive work has been performed to tackle image spam but with unsatisfactory results. Various algorithms and data augmentation techniques are used today to develop an optimal model for image spam recognition. Many of these proposed systems come close to the ideal system but do not provide 100 percent accuracy. This paper highlights the role of three popular techniques in image spam filtering. We discuss the importance and application of Optical Character Recognition, Support Vector Machines and, Artificial Neural Networks in unsolicited visual data filtering. This paper sheds light on the algorithms of these techniques. We provide a comparison of their accuracy, which helps us draw useful insights for developing a robust unsolicited visual data classification system. This paper aims to bring clarity regarding the feasibility of using these techniques to develop an unsolicited visual data filtering system. This paper records that the most favourable results are obtained using Artificial Neural Networks.

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Image spam filtering using convolutional neural networks

Cited by 21 publications

References 15 publications

Deep convolutional neural network based image spam classification

Deep convolutional neural network based image spam classification

SentiFilter: A Personalized Filtering Model for Arabic Semi-Spam Content based on Sentimental and Behavioral Analysis

Image Spam Filtering using Machine Learning Techniques

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