Abstract
With the proliferation of digital video content and the advancements in video editing tools, video forgery has become a critical concern in various domains, including journalism, surveillance, and legal proceedings. Detecting forged videos is a challenging task due to the increasing sophistication of forgery techniques. Traditional techniques include forensic watermarking, temporal inconsistencies analysis, and sensor pattern noise analysis. This research paper proposes a novel deepfake detection approach combining ResNet-50 and LSTM networks, a deep learning architecture known for its excellent performance in image recognition tasks. The proposed method leverages the strengths of both spatial and temporal modeling to enhance the detection accuracy. The ResNet-50 model is utilized to extract spatial features from individual frames, capturing visual cues and inconsistencies introduced by deepfake manipulations. The LSTM network is employed to model the temporal dependencies between frames, enabling the detection of subtle temporal artifacts that may indicate the presence of deepfakes. To train the model, a large-scale dataset comprising both real and deepfake videos is utilized. The dataset is carefully curated, ensuring a diverse range of deepfake manipulations and real-world scenarios. The ResNet-50 backbone is pre-trained on a large image dataset, allowing it to learn generic visual representations that are then fine-tuned for deepfake detection. The LSTM network is trained to capture the temporal dynamics and patterns specific to deepfake videos.
Keywords
CNN, Deepfake Detection, LSTM, ResNet50, Video Forgery Detection
Citations
IRE Journals:
Apurv Jindal
"Deepfake Video Forgery Detection" Iconic Research And Engineering Journals Volume 6 Issue 12 2023 Page 765-768
IEEE:
Apurv Jindal
"Deepfake Video Forgery Detection" Iconic Research And Engineering Journals, 6(12)
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