DeepFake Image Detection

The growth of deepfakes in today’s digital environ- ment raises significant doubts regarding the genuineness and dependability of the content found. To overcome this new challenge, Developing an effective method in the context of detection of deep images. In this study, we conduct a comparative analysis of three varied convolutional neural networks (CNNs) for deepfake image detection. Our experimental results highlight the strengths and weaknesses of each CNN architecture. We deliberate on the consequences of our results in the context of deep image perception and show which models may be better for certain situations. We also address the challenges and limitations associated with deep learning, such as the arms race of deep learning technologies and tools. In conclusion, our work adds to the expanding body of knowledge regarding deep image detection by comparing three major CNN architectures. Our findings provide important guidance for researchers, prac- titioners, and policymakers working to improve the security and authenticity of content in an increasingly digital age. As deepfake technology continues to evolve, the information presented in this study sets the groundwork for development of more powerful and updated deepfake detection mechanisms. Keywords—Deepfake, image detection, convolutional neural networks (CNNs), ResNet, InceptionV3, DenseNet, face forgery detection, GAN, forensics, deep learning, artificial intelligence, convolutional layers, pooling layers, fully connected layers, augmented data, accuracy, precision, recall, F1 measure, Area Under Receiver Operating Characteristic Curve.