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.

DeepImageSpam: Deep Learning based Image Spam Detection

While it is clear that working to deconstruct and reframe irrational thoughts can help bring positive perspective shifts to those suffering from mental distress, the specific factors that precipitate a moment of cognitive change are unclear, particularly in peer-to-peer support. Through data from an on- line mental health forum, we study patterns of peer support to shed light on how moments of cognitive change happen in online communities. Consistent with psychological literature, we use markers of language associated with moments of change, and explore how temporal changes in sentiment surrounding specific topics can approximate cognitive restructuring processes. In addition, we find that culture mediates the effect of these features. Based on the above findings, we propose a predictive model that can distinguish which conversation thread or a post is associated with a moment of cognitive change, and discuss the implications of our work for forum design and online peer support


MIC 2018 Conference Submissions

DeepImageSpam: Deep Learning based Image Spam Detection

Moment of change: Predicting Cognitive Restructuring in Online Mental Health Forums