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Multi-Scale Attention-based deepfake image classification for enhanced data protection

algorithms-and-analysis-of-algorithms
artificial-intelligence
computer-vision
security-and-privacy
neural-networks

Abstract

This paper introduces a multi-scale attention-driven deepfake classification framework to strengthen data integrity and cybersecurity. Utilizing state-of-the- art convolutional architectures—Xception convolutional architectures—Xception architectures—MobileNetV2, DenseNet121, and InceptionV3—the proposed method enhances feature extraction and adversarial robustness. A diverse dataset (FaceForensics++, 21,946 human face images) was employed, with images pre-processed to 224×224 resolution to standardize model input. By integrating multi-scale attention mechanisms, the approach refines spatial feature representation, improving discriminative capability against adversarial manipulation. Experimental evaluation confirms superior classification accuracy and flexibility against deep-fake manipulation, positioning this method as a robust advancement in automated forensic analysis and deepfake mitigation strategies.
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