Abstract
Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models the need for real-time detection demands greater efficiency. With this in mind unlike previous work we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover our method incorporates Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) as additional channel features to uncover manipulation traces in frequency and texture domains. Evaluations on COCOFake DFFD and CIFAKE datasets demonstrate our method’s state-of-the-art performance in most scenarios with a significant efficiency gain of up to a 20xreduction in FLOPs during inference. Finally by exploring BNNs in deepfake detection to balance accuracy and efficiency this work paves the way for future research on efficient deepfake detection.
Anxhelo Diko
PhD Student In Computer Science
A highly motivated and results-oriented Computer Vision Ph.D. student with a deep passion for advancing the field of artificial intelligence. My research focuses on building multimodal representations and understanding human activities, addressing key challenges for autonomous agents and AI in general. I have extensive experience with multimodal large language models for video captioning and question answering and a keen interest in view-invariant video representation learning. I am particularly committed to exploring how to effectively bridge the gap between representations of different modalities while preserving their unique characteristics. In addition to my research expertise, I have a strong engineering foundation honed through academic and industry experiences. Proficient in Python, C++, and CUDA, I excel at rapidly prototyping and implementing innovative ideas. I am eager to leverage my skills and knowledge to contribute to cutting-edge research and development in this dynamic field.