Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3091 2019 2019 Quantum University, Roorkee, Uttarakhand Ph.D. Scholar, India; Department of MCA, ABES Engineering College, Ghaziabad, Uttar Pradesh, India Aditi Aditi Dean Research Quantum University, Roorkee, Uttarakhand, India Amit Dixit Department of Computer Sc. and Eng., Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India Aditi Sharma 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. 2025 2025 62 76 10.54216/JCIM.150106 https://www.americaspg.com/articleinfo/2/show/3091