International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Deepfake images, particularly those generated through face-swapping techniques, have become increasingly realistic and widespread, raising serious concerns about digital trust, personal privacy, and public safety. As these manipulated visuals grow more sophisticated, detecting them reliably has become a pressing challenge. This paper proposes a deep learning-based approach for the automatic detection of face-swapped deepfakes using Convolutional Neural Networks (CNNs). Our method focuses on identifying subtle visual cues and inconsistencies introduced during the face manipulation process—artifacts that are often imperceptible to the human eye. To enhance detection accuracy and robustness, we integrate advanced image preprocessing, feature extraction, and data augmentation techniques. The model is trained and evaluated on widely used benchmark datasets containing a mix of authentic and manipulated images. Experimental results demonstrate high accuracy and generalization capability, reinforcing the practical value of the proposed solution for real-world applications in digital content verification. By automating the detection process, this work contributes meaningfully to the field of media forensics and supports ongoing efforts to preserve the authenticity and integrity of visual media in the age of synthetic content.

Keywords: Deepfake Detection, Face-Swapping, Convolutional Neural Networks (CNN), Image Forensics, Synthetic Media, Digital Content Verification, Image Preprocessing, Media Integrity, AI-generated Images, Feature Extraction.

Downloads: | DOI: 10.17148/IJARCCE.2025.141112