Real vs Fake Faces: DeepFakes and Face Morphing

The ability to determine the legitimacy of a person’s face in images and video can be important for many applications ranging from social media to border security. From a biometrics perspective, altering one’s appearance to look like a target identity is a direct method of attack against the security of facial recognition systems. Defending against such attacks requires the ability to recognize them as a separate identity from their target. Alternatively, a forensics perspective may view this as a forgery of digital media. Detecting such forgeries requires the ability to detect artifacts not commonly seen in genuine media. This work examines two cases where we can classify faces as real or fake within digital media and explores them from the perspective of the attacker and defender. First, we will explore the role of the defender by examining how deepfakes can be distinguished from legitimate videos. The most common form of deepfakes are videos which have had the face of one person swapped with another, sometimes referred to as “face-swaps.” These are generated using Generative Adversarial Networks (GANs) to produce realistic augmented media with few artifacts noticeable to human observers. This work shows how facial expression data can be extracted from deepfakes and legitimate videos to train a machine learning model to detect these forgeries. Second, we will explore the role of the attacker by examining a problem of increasing importance to border security. Face morphing is the process by which two or more peoples’ facial features may be combined in one image. We will examine the process by which this can be done using GANs, and traditional image processing methods in tandem with machine learning models. Additionally, we will evaluate their effectiveness at fooling facial recognition systems

Deep Insights of Deepfake Technology : A Review

Under the aegis of computer vision and deep learning technology, a new emerging techniques has introduced that anyone can make highly realistic but fake videos, images even can manipulates the voices. This technology is widely known as Deepfake Technology. Although it seems interesting techniques to make fake videos or image of something or some individuals but it could spread as misinformation via internet. Deepfake contents could be dangerous for individuals as well as for our communities, organizations, countries religions etc. As Deepfake content creation involve a high level expertise with combination of several algorithms of deep learning, it seems almost real and genuine and difficult to differentiate. In this paper, a wide range of articles have been examined to understand Deepfake technology more extensively. We have examined several articles to find some insights such as what is Deepfake, who are responsible for this, is there any benefits of Deepfake and what are the challenges of this technology. We have also examined several creation and detection techniques. Our study revealed that although Deepfake is a threat to our societies, proper measures and strict regulations could prevent this

Detecting Deepfake Videos Using Euler Video Magnification

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.Comment: Presented at Electronic Imaging: Media Watermarking, Security, and Forensics, 27 January 2021, 6 pages, 6 figure