Deepfake Detection: Top AI-Based Tools & Techniques

Deepfake Detection: Top AI-Based Tools & Techniques post thumbnail image

Deepfake material is formed by using two AI algorithms that compete with one another; the first is known as the generator, and the second is known as the discriminator. The generator responsible for creating the fake multimedia material will inquire of the discriminator as to whether or not the content is genuine or manufactured.

The network created by the combination of the generator and the discriminator is a generative adversarial network (GAN). When the discriminator correctly identifies a piece of material as false. The generator receives helpful feedback on making the next deepfake even more convincing.

The first thing that needs to be done to set up a GAN is to determine the output that will be needed and then to develop a dataset for the generator to learn from. After the generator has reached a satisfactory output level, video clips can be introduced into the discriminator. The generator and the discriminator become better at their jobs as time passes.

The discriminator and the generator become better at their jobs as time passes. The generator gets better at making fake video clips, while the discriminator gets better at finding them. On the other hand, as the discriminator improves its ability to recognize fake videos. The generator improves its own ability to create such videos. The following are some of the most effective tools for identifying deepfake videos.

Microsoft’s Video Authenticator Tool

This video authenticator tool was developed by Microsoft and released in September 2020. It can analyze a video or still photograph and produce a confidence score to determine whether the media has been modified. It can identify the blending border of the deepfake as well as minor grayscale features that are not visible to the naked eye. In addition, it offers this confidence score in a timely manner.

The application was developed utilizing a dataset available to the public from Face Forensics++ and validated with the Deepfake Detection Challenge Dataset. Both of these datasets are industry-leading technologies that are used for training and testing deepfake detection algorithms.

Deepfake Detection: Microsoft's Video Authenticator Tool
Deepfake Detection

The industry behemoth that specializes in information technology has also released a new piece of software that can detect doctored material and reassure users of its genuineness. It is composed of two parts: the first is an integration with Microsoft Azure that enables the content creator to store digital hashes and certificates in the part of its metadata that is preserved. The second is a component that assists the reader in verifying and matching these certificates and hashes to determine whether or not the content is authentic.

Biological Signals Deepfake Detection

Researchers from Intel and Binghamton University have developed the tool that recognizes the deepfake model hidden beneath the compromised video. This goes beyond the capabilities of traditional deepfake detection methods. This program searches for one-of-a-kind biological and generative noise signals known as “deepfake heartbeats,” which are left behind by deepfake model videos. Photoplethysmography cells are located in 32 distinct locations on a person’s face and are responsible for detecting these signals.

Convolutional neural networks, including VGG blocks, serve as the foundation for this model’s design. It uses the Python OpenFace module for face detection, the OpenCV image processing library, and the Keras neural network implementation library. In the same way as the learning setting of the Video Authenticator tool from Microsoft is based on the FaceForensics++ dataset, the learning configuration of FaceCatcher is also. According to the researchers’ findings, this technology has an accuracy detection of 97.29% when detecting fake videos.

Deepfake Detection Using Phoneme-Viseme Mismatches

Researchers from the University of California and Stanford University are responsible for developing this model. This method uses the fact that visemes, representing the dynamics of the mouth shape, are not always the same as the spoken phoneme and may sometimes even be inconsistent.

For instance, when pronouncing words like “mama” and “papa,” there could be a phoneme-viseme mismatch. This may be utilized to identify even geographically minor and temporally localized changes in deepfake videos. The researchers used Text-to-Video for Short Utterances, Audio-to-Video, and Text-to-Video for Longer Utterances deepfakes to construct lipsync deepfakes. These deepfakes were created utilizing three different synthesis techniques.

The method was used for both human and automated methods of video authentication. This model demonstrated an accuracy of 96.0%, 97.8%, and 97.4% when it came to human authentication for A2V, T2V-S, and T2V-L. While it showed an accuracy of 93.4%, 97.0%, and 92.8% when it came to automated authentication, respectively.

Forensic Technique Using Facial Movements

This model analyzes the facial motions and expressions of a single video supplied as input. It determines the existence of certain action units as well as their strength. This detection model utilizes a one-class support vector model that can differentiate comic impersonators from deepfake impersonators. Additionally, it can tell the difference between one individual and other impersonators.

This model extracts face and head motions from a video using OpenFace. A toolset for analyzing facial behavior that is available under an open-source license. The face landmark locations, head poses, and facial action units for each frame are all provided by the library in both 2D and 3D formats.

Deepfake Detection: Forensic Technique Using Facial Movements
Deepfake Detection

Recurrent Convolutional Strategy

This looks for face modification in videos by using a technique called recurrent convolutional models.  RCMs are a type of deep learning model that efficiently utilizes temporal information from picture streams across domains. Face2Face, Deepfake, and FaceSwap altered faces could all be uncovered with this technology, which analyzes video streams. It was evaluated using the FaceForensics++ dataset, where it demonstrated an accuracy of up to 97%. This is a 4.55% increase over the techniques that came before it.

Conclusion

The process of making deepfakes is always evolving and becoming more sophisticated. This listicle does not offer the tools and techniques described as having total accuracy and efficacy. Despite this, they are progressing in the correct direction, which is important for a much larger situation.