A survey on deepfake detection through deep learning

Imagine watching a video where Tom Hanks delivers a rousing speech, but you suspect it might be fabricated. This growing concern stems from the rise of "DeepFakes," hyper realistic manipulated videos created using deep learning algorithms. These tools can seamlessly stitch together faces, voices, and body movements, blurring the lines between reality and fiction. While DeepFakes hold promise for entertainment and creative expression, their potential for misuse is significant. Malicious actors could leverage them to spread misinformation, damage reputations, or even influence elections. Thankfully, researchers are developing sophisticated techniques to detect these synthetic creations. This survey delves into the realm of DeepFake detection, exploring various methods employed by deep neural networks (DNNs). We'll dissect how DeepFakes are made, categorize the most common creation techniques, and analyze the strengths and weaknesses of different detection approaches. Furthermore, we'll examine the evolving landscape of DeepFake datasets, which fuel the training and testing of detection models. We'll also discuss the ongoing quest for a universal DeepFake detector, capable of identifying even unseen manipulations. Finally, we'll touch on the ongoing challenges facing both DeepFake creators and detectors, highlighting the arms race that is unfolding in this technological battleground. By shedding light on these advancements and obstacles, we hope to empower audiences with the knowledge to critically evaluate the information they encounter in the digital age.