In the software development life cycle, the implementation of stringent security requirements is essential to promote the creation of robust and secure code, thereby avoiding the need for extensive post-implementation revisions. A wide variety of methodologies are commonly employed to examine source code authorship, ranging from adherence to strict standards and guidelines to the application of best practices. However, these reviews are often very laborious and demand a broad spectrum of specialized knowledge from various DevOps task groups to effectively address underlying vulnerabilities. To streamline and enhance the efficiency of the review process, advanced Machine Learning techniques are increasingly being adopted as a critical factor in improving the precision of transitions to secure code structures. This manuscript introduces an innovative transformation system that leverages the contextual adaptability provided by the renowned advanced language model, CodeBERT, integrated with a Generative Adversarial Network (GAN). This synergistic combination allows for the precise classification of insecure code segments in different programming languages and the subsequent generation of their secure counterparts. Empirical results confirm the system’s ability to detect up to 98.3% of insecure tokens and reconstruct secure versions with an accuracy of up to 95.67%.

In recent years, the darknet, a hidden part of the deep web associated with illicit activities, has been the subject of study due to the myths and mysteries surrounding it. Contemporary research aims to uncover the true topics hidden within this network using thematic analysis techniques, which are essential for cybercrime prevention and legal action. However, the dynamic and anonymous nature of the darknet poses the challenge of effectively navigating the TOR protocol to obtain and analyze samples from hidden sites. This paper presents an innovative approach to studying the darknet. Assuming limited prior knowledge of the original topics, a contextual relation-comparison technique with TinyBERT, a large language model, is used to generate super topics from previously identified hidden sites. From these super topics, keywords with contextual scores and weights are extracted, serving as input for a sensor that navigates the TOR network and aggregates new hidden sites. These sites are processed through semi-supervised learning to form clusters of sub-topics. Labels for each sub-topic propagate based on their similarity to the main topics and are ultimately classified in a fine-tuning layer of TinyBERT. The results demonstrate the identification of twelve classes of sub-topics in the darknet, related to drugs, hacking, marketplaces, pornography, and other areas, with a classification accuracy of 95.45%.

The rapid advancement in deepfake technology has enabled the creation of highly realistic fake images and videos, posing significant risks, especially in the context of explicit content. Such content, which often involves the alteration of an individual’s identity in sexually explicit material, can lead to defamation, harassment, and blackmail. This paper focuses on the detection of deepfakes in explicit content using a state-of-the-art ID-unaware Binary Classification method. We evaluate its effectiveness in real-world scenarios by analyzing three versions of the model with different backbones: ResNet34, EfficientNet-B3, and EfficientNet-B4. To facilitate this evaluation, we curated a dataset of 200 videos, consisting of 100 genuine videos and their corresponding deepfake counterparts, ensuring a direct comparison between genuine and altered content. Our analysis revealed a significant decrease in detection performance when applying the state-of-the-art method to explicit content. Specifically, the AUC score dropped from 93% on standard datasets such as FaceForensics++ to 62% on our explicit content dataset. Additionally, the accuracy for detecting deepfakes plummeted to around 25%, while the accuracy for genuine videos remained high at approximately 90%. We identified specific factors contributing to this decline, including unconventional makeup, lighting issues, and facial blurring due to camera distance. These findings underscore the challenges and the necessity for robust detection methods to address the unique problems posed by explicit content deepfakes, ultimately aiming to protect individuals from the potential harms associated with this technology.

Nowadays, fingerprint is the biometric more implemented to authentication and recognition of people for governmental and private purposes. This paper aims present the implementation of a fingerprint recognition system based only on bifurcation minutiaes and singularities to create a template, the template obtained is stored and used on recognition and verification tasks. The evaluation of the proposed system shows that using the bifurcation minutiaes the system provides high results and a good performance, the results were obtained in recognition and verification ways and the processing time was measured via an user interface.

Retrieving information from social networks is a first and primordial step in many data analysis fields such as Natural Language Processing and Machine Learning. Important data science tasks rely on historical data gathering for further predictive results. Recent works use public platforms for collecting public streams of information like Twitter API, which allows querying chronological tweets from periods no longer than three weeks. In this paper, we present Twitter Scrapy, a new methodology for collecting historical tweets from time periods of arbitrary duration using web scraping techniques that bypass Twitter API restrictions.

This paper proposes a novel methodology for change detection in video sequences, which consists in the use of projection of the first eigenvector over the current frame in the video sequence. These eigenvectors are computed using the Incremental Principal Component Analysis (IPCA), assuming that the incremental computation of the eigenvalues and eigenvectors is made using the incremental block approach considering only two frames i.e. the past and the current frames in each incremental block. The main contribution of this work, is the use of the idea that the first eigenvector projects the maximum variability in their data matrix and then by using the incremental block of two frames in the IPCA, the maximum variability in those images could be considered as the change between them; such that after the post-processing in the projected matrix, we are able to labeled the change between the past and the current frames.