  Hackers and spammers are employing innovative and novel techniques to deceive
novice and even knowledgeable internet users. Image spam is one of such
technique where the spammer varies and changes some portion of the image such
that it is indistinguishable from the original image fooling the users. This
paper proposes a deep learning based approach for image spam detection using
the convolutional neural networks which uses a dataset with 810 natural images
and 928 spam images for classification achieving an accuracy of 91.7%
outperforming the existing image processing and machine learning techniques
