In the recent years, social media has grown to become a major source of information for many online users. This has given rise to the spread of misinformation through deepfakes. Deepfakes are videos or images that replace one persons face with another computer-generated face, often a more recognizable person in society. With the recent advances in technology, a person with little technological experience can generate these videos. This enables them to mimic a power figure in society, such as a president or celebrity, creating the potential danger of spreading misinformation and other nefarious uses of deepfakes. To combat this online threat, researchers have developed models that are designed to detect deepfakes. This study looks at various deepfake detection models that use deep learning algorithms to combat this looming threat. This survey focuses on providing a comprehensive overview of the current state of deepfake detection models and the unique approaches many researchers take to solving this problem. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.

In today's world, artificial intelligence can be used to create what's called "deep fakes."

When celebrity porn and other deepfake videos went viral several years back they caught the world largely unprepared -- few could believe just how convincingly AI had generated the fake images. We have since seen numerous breakthroughs in image synthesis algorithms and face-synthesizing and swapping technologies enabled by generative adversarial networks (GANs), making deepfakes even more believable. Governmental and other bodies meanwhile have been scrambling to catch up -- looking for ways to counter the malicious spread of deepfakes which are now so realistic they can be difficult if not impossible for the human eye to detect. A team of researchers from MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) have proposed a new model that is designed to spot deepfakes by looking at subtle visual artifacts such as textures in hair, backgrounds, and faces, and visualizing image regions where it has detected manipulations. The team noted that as SOTA image synthesis techniques continue advancing under novel synthesis algorithms, it is critical that fake image detection methods keep step to enable efficient and robust identification of deepfakes created using such new methods. In this regard it is important to understand which artifacts the fake image detectors will examine if they are to remain effective in the face of continually evolving synthesis algorithms.

Ai Editorial: Deepfakes supported by AI techniques today are considered to be a growing problem. It is vital to build AI systems that can automated deepfake detection so that risks such as identity fraud can be tackled, writes Ai's Ritesh Gupta Artificial intelligence (AI)-based identity fraud is emerging as a serious issue. Recognition of one's voices and face as a way to validate a person's identity is under scrutiny with the rise of synthetic media and deepfakes. Be it for security-related risks, user privacy concerns or fraudulent transactions, repercussions are being probed at this juncture. Technology to manipulate images, videos and audio files is progressing faster than one's ability to tell what's real from what's been faked.

Deepfake videos are hard for untrained eyes to detect because they can be quite realistic. Whether used as personal weapons of revenge, to manipulate financial markets or to destabilize international relations, videos depicting people doing and saying things they never did or said are a fundamental threat to the longstanding idea that "seeing is believing." Most deepfakes are made by showing a computer algorithm many images of a person, and then having it use what it saw to generate new face images. At the same time, their voice is synthesized, so it both looks and sounds like the person has said something new. Some of my research group's earlier work allowed us to detect deepfake videos that did not include a person's normal amount of eye blinking – but the latest generation of deepfakes has adapted, so our research has continued to advance.