Copy-move image forged information detection and localisation in digital images using deep convolutional network

Image tempering is one of the significant issues in the modern era. The use of powerful tools for image editing with advanced technology and its widespread on social media raised questions on data integrity. Currently, the protection of images is uncertain and a severe concern, mainly when it transfers over the Internet. Thus, it is essential to detect an anomaly in images through artificial intelligence techniques. The simple way of image forgery is called copy-move, where a part of an image is replicated in the same image to hide unwanted content of the image. However, image processing through handcrafted features usually looks for pattern concerns with duplicate content, limiting their employment for huge data classification. On the other side, deep learning approaches achieve promising results, but their performance depends on training data with fine-tuning of hyperparameters. Thus, we proposed a custom convolutional neural network (CNN) architecture with a pre-trained model ResNet101 through a transfer learning approach. For this purpose, both models are trained on five different datasets. In both cases, the impact of the model is evaluated through accuracy, precision, recall, F-score and achieved the highest 98.4% accuracy using the Coverage dataset.

Warrant Generation through Deep Learning

The warrant element of the Toulmin model is critical for fact-checking and assessing the strength of an argument. As implicit information, warrants justify the arguments and explain why the evidence supports the claim. Despite the critical role warrants play in facilitating argument comprehension, the fact that most works aim to select the best warrant from existing structured data and labelled data is scarce presents a fact-checking challenge, particularly when the evidence is insufficient, or the conclusion is not inferred or generated well based on the evidence. Additionally, deep learning methods for false information detection face a significant bottleneck due to their training requirement of a large amount of labelled data. Manually annotating data, on the other hand, is a time-consuming and laborious process. Thus, we examine the extent to which warrants can be retrieved or reconfigured using unstructured data obtained from their premises.

Nonparametric inference of stochastic differential equations based on the relative entropy rate

The information detection of complex systems from data is currently undergoing a revolution,driven by the emergence of big data and machine learning methodology. Discovering governingequations and quantifying dynamical properties of complex systems are among central challenges. Inthis work, we devise a nonparametric approach to learn the relative entropy rate from observationsof stochastic differential equations with different drift functions. The estimator corresponding tothe relative entropy rate then is presented via the Gaussian process kernel theory. Meanwhile, thisapproach enables to extract the governing equations. We illustrate our approach in several examples.Numerical experiments show the proposed approach performs well for rational drift functions, notonly polynomial drift functions.

Video Scene Information Detection Based on Entity Recognition

Video situational information detection is widely used in the fields of video query, character anomaly detection, surveillance analysis, and so on. However, most of the existing researches pay much attention to the subject or video backgrounds, but little attention to the recognition of situational information. What is more, because there is no strong relation between the pixel information and the scene information of video data, it is difficult for computers to obtain corresponding high-level scene information through the low-level pixel information of video data. Video scene information detection is mainly to detect and analyze the multiple features in the video and mark the scenes in the video. It is aimed at automatically extracting video scene information from all kinds of original video data and realizing the recognition of scene information through “comprehensive consideration of pixel information and spatiotemporal continuity.” In order to solve the problem of transforming pixel information into scene information, this paper proposes a video scene information detection method based on entity recognition. This model integrates the spatiotemporal relationship between the video subject and object on the basis of entity recognition, so as to realize the recognition of scene information by establishing mapping relation. The effectiveness and accuracy of the model are verified by simulation experiments with the TV series as experimental data. The accuracy of this model in the simulation experiment can reach more than 85%.