Enhancing Digital Integrity with Hybrid Deepfake Detection for Images and Videos

Deepfake technology, powered with highly advanced machine learning algorithms, has transformed the generation of digital content, but at the same time, it raises a lot of serious security and ethical concerns. Keeping these threats in mind, a novel framework is proposed for the detection of deepfakes by integrating CNNs, LSTM networks, and ResNet50. CNNs give spatial features of the image and video frames while LSTMs provide the capabilities of capturing temporal dependencies, which help in improving detection accuracy. Together, the deep residual architecture of ResNet50 fortifies the representation of features and eventually sues the model against adversarial deepfake techniques. Accuracy and resilience evaluation against various state-of-the-art synthetic media generation would further prove model efficacy based on comprehensive evaluation across multiple deepfake datasets. Hence, this study aids in the advancement of methodology regarding deepfake detection by offering a scalable and efficient approach, thereby upholding integrity in digital content while also averting risks of misinformation and fraud.