Efficiently Identifying Fake Audio And Images Using Transfer Learning

Abstract

With the advent of AI-generated content in today's digital world, identifying forged images and audio has become more difficult. The system suggested here utilizes deep learning to enhance the precision and dependability of detecting fake media. A strong Convolutional Neural Network (CNN) known as VGG19 is utilized to label fake images. VGG19 uses deep features toanalyze images and detect discrepancies in textures, illumination, and pixel patterns that signal manipulation. The fine-tuned pre-trained VGG19 model with a large database of real and fake images improves prediction accuracy. For detecting fake audio, the system employs a Recurrent Neural Network (RNN), which is best suited for processing sequential data. By analyzing spectrogram features and waveform patterns, the RNN model detects anomalies in pitch, tone, and frequency typical of AI-generated or manipulated audio. The model is also trained on synthetic and real speech datasets to identify authentic and deepfake audio successfully. Through the combination of VGG19 for image detection and RNN for audio classification, the suggested system offers a powerful method for fake multimedia content detection. The proposed solution improves security, digital forensics, and misinformation avoidance, providing more trustworthy authentication of visual and audio data in real-world applications.