Today’s technological advancements in the field of digital media have resulted in the unprecedented transmission of information leading to unauthorized exploitation. Businesses use social media as the primary marketing platform. Considering the severity of spreading misinformation and fake news in our society due to false marketing by bogus businesses, there is a great need to demystify this propagation using web forensics-based frameworks. In order to increase consumer equity, the rapid spreading of malicious information makes it hard for users to differentiate between real and false information. This research intends to design an effective and adaptable framework for detecting false information campaign carried out by criminals affecting online social network (ONS). A novel ForenOptiNet model is designed and diverse data gathered from the Reddit and INFD dataset is used to train the suggested model. The Web Forensic-Based Investigation Optimization (WFBIO) algorithm provides a high accuracy classification of malicious content from the web. Moreover, the WFBIO framework enhances the robustness of the ForenOptiNet model and ensures that the proposed approach can effectively identifies misinformation and fake news to validate factual claims. Results of the simulation analysis provides a muti-level mechanism combining anomaly detection and ForenOptiNet model together outperforming other state-of the-art optimization algorithms trained against CNNs with SGD, Adagrad and AdaDelta. While these baselines yielded accuracies between 55 and 92%, our proposed model achieved highest accuracy of 99% accuracy with an effective front-end design integration.

Copy detection is an important component of digital rights management and can be implemented using a retrieval-based approach. Under this approach, a query image, suspected to be a copy, is compared against all the images in the owner database. The comparison is done based on a distance metric in feature space. The performance of such a system depends on the mutual separation of the feature representation of the images in the database. In this paper we propose a framework that increases this mutual separation by literally shifting them away from each other. The idea of modifying the features derives its inspiration from the field of watermarking. It is also important to make sure that the semantics of the images do not change after modification. Thus the focus of this paper is on how to modify the images in the database, so that the mutual separation between the images in feature space is above a certain threshold and the distortion induced is minimized. This problem can be formulated as a non-convex optimization problem which is difficult to solve. We propose a restriction of the problem and solve it using second-order cone programming. We present a practical implementation of our framework, named RAM, which uses AFMT as the feature representation. We conduct experiments to test the performance of RAM.

based processing of radiological images". Different imaging modalities, for example X-Ray CT and NMR, generate markedly different images from the same anatomy. Investigating the nature of the images produced has allowed us to develop a low level domain independent segmentation algorithm. A scheme to perform further merging has been developed, based on rules and a current world model derived from the anatomy, the modality and non-visual knowledge^. This final stage produces a segmented world representation of the image which is directly comparable to the original current world model. The X-Ray CT and NMR imaging techniques provide a non-invasive window for the examination of in