Question: Which of these people are fake? University at Buffalo deepfake spotting tool proves 94% effective with portrait-like photos, according to study. University at Buffalo computer scientists have developed a tool that automatically identifies deepfake photos by analyzing light reflections in the eyes. The tool proved 94% effective with portrait-like photos in experiments described in a paper accepted at the IEEE International Conference on Acoustics, Speech and Signal Processing to be held in June in Toronto, Canada. "The cornea is almost like a perfect semisphere and is very reflective," says the paper's lead author, Siwei Lyu, PhD, SUNY Empire Innovation Professor in the Department of Computer Science and Engineering.
Computer scientists have developed a tool that detects deepfake photos with near-perfect accuracy. The system, which analyzes light reflections in a subject's eyes, proved 94 percent effective in experiments. In real portraits, the light reflected in our eyes is generally in the same shape and color, because both eyes are looking at the same thing. Since deepfakes are composites made from many different photos, most omit this crucial detail. Deepfakes became a particular concern during the 2020 US presidential election, raising concerns they'd be use to discredit candidates and spread disinformation.
In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes. The availability of open-source tools to create DeepFakes poses as a threat to the trustworthiness of the online media. In this work, we develop an open-source online platform, known as DeepFake-o-meter, that integrates state-of-the-art DeepFake detection methods and provide a convenient interface for the users. We describe the design and function of DeepFake-o-meter in this work.
Recent advances in artificial intelligence make it progressively hard to distinguish between genuine and counterfeit media, especially images and videos. One recent development is the rise of deepfake videos, based on manipulating videos using advanced machine learning techniques. This involves replacing the face of an individual from a source video with the face of a second person, in the destination video. This idea is becoming progressively refined as deepfakes are getting progressively seamless and simpler to compute. Combined with the outreach and speed of social media, deepfakes could easily fool individuals when depicting someone saying things that never happened and thus could persuade people in believing fictional scenarios, creating distress, and spreading fake news. In this paper, we examine a technique for possible identification of deepfake videos. We use Euler video magnification which applies spatial decomposition and temporal filtering on video data to highlight and magnify hidden features like skin pulsation and subtle motions. Our approach uses features extracted from the Euler technique to train three models to classify counterfeit and unaltered videos and compare the results with existing techniques.
Have you seen Barack Obama call Donald Trump a "complete dipshit", or Mark Zuckerberg brag about having "total control of billions of people's stolen data", or witnessed Jon Snow's moving apology for the dismal ending to Game of Thrones? Answer yes and you've seen a deepfake. The 21st century's answer to Photoshopping, deepfakes use a form of artificial intelligence called deep learning to make images of fake events, hence the name deepfake. Want to put new words in a politician's mouth, star in your favourite movie, or dance like a pro? Then it's time to make a deepfake.