Abstract :

The Rapid Advancement Of Deep Learning–based Manipulation Techniques Has Led To Highly Realistic Object-based Video Forgeries, Posing Significant Threats To Digital Security, Media Authenticity, And Public Trust. Existing Studies Reveal That Modern DeepFake And Tampering Methods Leave Subtle Visual, Temporal, And Semantic Inconsistencies That Can Be Effectively Analyzed Using Deep Neural Networks [1], [2], [3]. Recent Works In Video Forensics Demonstrate The Importance Of Detecting Warping Artifacts, Head-pose Inconsistencies, And Spatio-temporal Distortions To Expose Manipulated Regions [1], [9], [11], [17]. Convolutional Neural Networks (CNNs), Two-stream Architectures, And Recurrent Networks Have Proven Highly Effective For Identifying Object Removal, Splicing, And GANgenerated Forgeries By Learning Deep Visual Representations And Motion Cues [6], [12], [18], [21]. Large Datasets Such As The DeepFake Detection Challenge (DFDC) Further Support Robust Model Training And Generalization Across Diverse Manipulation Styles [13]. Additionally, Recent Research Highlights The Relevance Of Exploiting Color Inconsistencies, Residual Noise Patterns, And Multi-scale Forensic Traces For Precise Forgery Localization [4], [10], [14], [22]. Object-level Manipulation Detection Is Strengthened By Combining Spatial CNN Features With Temporal Modeling, Enabling The System To Capture Fine-grained Tampering Artifacts Across Consecutive Frames [15], [19], [24]. Despite Adversarial Attacks And Evolving Forgery Methods That Challenge CNN Robustness [23], Deep Convolutional Strategies—enhanced With Recurrent Layers And Multi-stream Analysis— Remain The Most Reliable Solution For Advanced Video Forgery Detection [7], [16], [25]. This Research Builds Upon These Findings To Develop A Robust, Deep Convolutional Neural Network Tailored For Detecting Complex Object-based Video Forgeries, Ensuring High Accuracy, Temporal Stability, And Resilience Against Modern Manipulation Techniques. Keywords : Deep Convolutional Neural Networks, Video Forgery Detection, ObjectBased Manipulation, DeepFake Detection, Spatio-Temporal Analysis, Video Forensics, CNN Features, Temporal Inconsistency, GANBased Forgeries, Tampering Localization, Motion Cues, Multimedia Security, Digital Forensics, Adversarial Robustness, Forgery Classification.

Published:

29-11-2025

Issue:

Vol. 25 No. 11 (2025)

Page Nos:

294-300

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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