The rise of AI-generated synthetic media, or deepfakes, has introduced unprecedented opportunities and challenges across various fields, including entertainment, cybersecurity, and digital communication. Using advanced frameworks such as Generative Adversarial Networks (GANs) and Diffusion Models (DMs), deepfakes are capable of producing highly realistic yet fabricated content, while these advancements enable creative and innovative applications, they also pose severe ethical, social, and security risks due to their potential misuse. The proliferation of deepfakes has triggered phenomena like "Impostor Bias", a growing skepticism toward the authenticity of multimedia content, further complicating trust in digital interactions. This paper is mainly based on the description of a research project called FF4ALL (FF4ALL-Detection of Deep Fake Media and Life-Long Media Authentication) for the detection and authentication of deepfakes, focusing on areas such as forensic attribution, passive and active authentication, and detection in real-world scenarios. By exploring both the strengths and limitations of current methodologies, we highlight critical research gaps and propose directions for future advancements to ensure media integrity and trustworthiness in an era increasingly dominated by synthetic media.

Mental illness is a health challenge faced by adolescents that has grown worse after the Coronavirus disease 2019 pandemic. Research on social media and young people's mental health has recently increased, and numerous studies have examined whether frequent use of social media is linked to issues such as anxiety, stress, depression, eating disorders, insomnia, frustration, feeling alone, and externalizing problems among adolescents. This influence of social media on adolescents' lives is clear, with many platforms like Facebook, Instagram, and YouTube playing an important role in daily interactions and self-expression. Even though social media offers numerous benefits, such as connectivity and information sharing, excessive usage can have detrimental effects on mental health, particularly among adolescents.

To study the impact of social media on the mental wellbeing of adolescents, and the associated potential dangers in India.

A total of 204 adolescents aged 14 years to 23 years were included in the study. This study explored the intricate relationship between social media usage and adolescent mental health in India. The study employs a cross-sectional survey design to capture a snapshot of adolescent mental health and social media usage patterns. Data collection involved administering structured questionnaires and the analysis utilized quantitative methods, including descriptive statistics.

Excessive use of social media is correlated with increased stress, anxiety, and depression. Adolescents engage in compulsive behaviors such as scrolling in the middle of the night, which negatively impacts their mental and physical health, and leads to significant sleep disruption. Findings from the study aim to provide insights into the current state of adolescent mental health and inform strategies to promote positive wellbeing in the Indian population.

The study underscores the need for further research to better understand the complex interplay between social media and adolescent mental health, and need for effective strategies to combat online harassment.

As manipulating images by copy-move, splicing and/or inpainting may lead to misinterpretation of the visual content, detecting these sorts of manipulations is crucial for media forensics. Given the variety of possible attacks on the content, devising a generic method is nontrivial. Current deep learning based methods are promising when training and test data are well aligned, but perform poorly on independent tests. Moreover, due to the absence of authentic test images, their image-level detection specificity is in doubt. The key question is how to design and train a deep neural network capable of learning generalizable features sensitive to manipulations in novel data, whilst specific to prevent false alarms on the authentic. We propose multi-view feature learning to jointly exploit tampering boundary artifacts and the noise view of the input image. As both clues are meant to be semantic-agnostic, the learned features are thus generalizable. For effectively learning from authentic images, we train with multi-scale (pixel / edge / image) supervision. We term the new network MVSS-Net and its enhanced version MVSS-Net++. Experiments are conducted in both within-dataset and cross-dataset scenarios, showing that MVSS-Net++ performs the best, and exhibits better robustness against JPEG compression, Gaussian blur and screenshot based image re-capturing.