Detection of Manipulated Multimedia In Digital Forensics Using Machine Learning

The surge in digital media usage has spurred an uptick in multimedia manipulation., spanning images., videos., and audio. This manipulation., with its potential to spread misinformation and manipulate public opinion., poses serious threats. Detecting genuine from fake media is challenging due to the diverse tools employed. Consequently., cybercrime involving manipulated media is on the rise. Researchers are countering this issue with machine learning techniques., particularly Convolutional Neural Networks (CNNs). This paper presents an application leveraging CNN s to identify genuine and fake media., bolstered by results from experiments on real and manipulated datasets., yielding high accuracy and robustness. Deep learning models excel in detecting various manipulation types., positioning them as potent weapons against manipulated content proliferation. To ascertain the models' effectiveness., the study includes comprehensive validation., testing procedures., and robustness analyses against sophisticated manipulations., including adversarial attacks and deepfake variations. This research advances multimedia forensics., offering a holistic approach to detect manipulated media with deep learning models., underscoring CNN s' effectiveness in curbing manipulated content dissemination., and emphasizing the necessity of ongoing advancements to tackle the evolving multimedia manipulation landscape.