Deeptracenet: An Ai Driven Framework For Mathematical Feature-based Forgery Detection

Abstract

The Rapid Growth Of Deepfake Technologies Has Raised Serious Concerns Regarding The Authenticity Of Digital Images And Videos, Impacting Information Security, Media Credibility, And Forensic Analysis. This Research Presents A Deep Learning-based Framework For Identifying AI-generated Manipulated Content With Improved Accuracy And Efficiency. A Hybrid Convolutional Neural Network (CNN) Architecture Is Employed, Where ResNet Serves As The Backbone For Extracting High-level Spatial Features From Input Media. The Model Is Enhanced With Inverted Residual Blocks And Linear Bottlenecks To Preserve Essential Feature Information While Reducing Computational Complexity And Memory Usage. The Proposed Approach Enables Automatic Learning Of Discriminative Features, Eliminating The Dependence On Manual Feature Engineering. Input Images And Video Frames Undergo Preprocessing Followed By Feature Extraction And Classification Stages To Determine Authenticity. The Integration Of Advanced Architectural Components Contributes To Faster Inference And Better Generalization Across Diverse Deepfake Manipulation Techniques. Experimental Considerations Indicate That The Framework Is Capable Of Effectively Distinguishing Between Real And Forged Content, Even When Visual Differences Are Minimal. Overall, This Research Provides A Robust And Scalable Solution For Deepfake Detection, Ensuring Reliable Identification Of Manipulated Media And Supporting The Preservation Of Digital Content Integrity.

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