Detecting Image Spam on Social Media Platforms Using Deep Learning Techniques

Himani Jain^{1, 2}, Amit Dixit³, Aditi Sharma^4,*

¹Quantum University, Roorkee, Uttarakhand Ph.D. Scholar, India

²Department of MCA, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

³Dean Research Quantum University, Roorkee, Uttarakhand, India

⁴Department of Computer Sc. and Eng., Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India

Email: himanijain1987ap@gmail.com; dixitamit777@gmail.com; aditi.sharma@ieee.org

Abstract

Image spam involves the practice of concealing text within an image.  Various machine-learning techniques are used to categories image spam, utilizing a wide range of features extracted from the images.   Convolutional neural networks (CNNs) are commonly used for image classification and feature extraction tasks because of their outstanding performance. In this study, our focus is to analyses image spam using a CNN model that incorporates deep learning techniques. This model has been meticulously fine-tuned and optimized to deliver exceptional performance in both feature extraction and classification tasks. In addition, we performed comparative evaluations of our model on different image spam datasets that were specifically created to make the classification task more challenging. The results we obtained show a significant improvement in classification accuracy compared to other methods used on the same datasets.

Keywords: Deep Learning; Image Spam; Spam Detection; CNN; Social Sites