The deepfake technology software market was valued at $72.41 million in 2023 and is primed to continue on a bullish run due to its value in entertainment, content creation, medicine, and marketing.
On the other side, there is a growing fear that Deepfakes pose significant threats to trust, security, and privacy to people and organizations due to their malicious applications and use.
To address these concerns, deepfake detector tools are springing up, offering different capabilities. What are the best AI deepfake detectors available in 2024? Here’s our list.
How Deepfake Detectors Work
One common approach involves analyzing facial features and expressions to pick out irregularities or inconsistencies that may indicate the presence of a deepfake.
Some deepfake detectors look at how color changes in a video, then turn these color changes into maps and use deep learning to tell if the video is real or fake.
Deepfake detectors can also listen to speech patterns in audio and look for signs that it’s been manipulated by AI.
Some deepfake detector solutions focus on the analysis of contextual information, such as unnatural lighting, shadows, or perspectives, to discern the authenticity of the scenes portrayed in a video or image content.
7 Best Deepfake Detectors
WeVerify is one of the best social media and web content deepfake detectors in the market.
Developed within the WeVerify project and launched in January 2019, it detects deepfakes using machine learning and AI algorithms, facial recognition software (FRS), and a blockchain-based public database of exposed fakes.
Its prominent features for exposing fabricated content include cross-modal verification, social network analysis, micro-targeted debunking, and intelligent human-in-the-loop content verification.
The free deepface detection tool can easily integrate with in-house content management systems for organizational use and offers a premium web platform-as-a-service.
6. Deepware Scanner
The Deepware scanner, developed by Zemana in 2018 following the Deepware AI project, runs on the EfficientNet-B7 model of the convolutional neural network architecture.
Deepware provides real-time authenticity verification for users using AI scanners that run over a hundred thousand videos across several open sources.
The scanner can spot manipulated faces but needs at least one real face in the content to do this. It can also find DeepFake videos that are up to 10 minutes long and give a detailed report on them.
Deepware uses a top-level Deepfake detection challenge dataset – the CFDF dataset – which contains over 120,000 consented tested videos that include Celeb-DF YouTube, MrDeepFakes, 4Chan Real, etc. The tool is also available as a mobile application.
5. Microsoft Video AI Authenticator
This deepfake detection tool was developed by the Microsoft Research and Responsible AI team.
In response to the widespread fake and manipulated media in the news during the 2020 US elections, Microsoft created this tool for analyzing videos and still photos in order to generate an accurate manipulation score.
The tool detects subtle grayscale changes that are usually missed by normal eyes and provides real-time confidence scores for fast detection of deepfakes.
To humanize the software, it used data from the Face Forensic++ public dataset and was tested on the DeepFake detection challenge dataset.
For wider adoption and use, Microsoft partnered with the San Francisco-based AI Foundation to provide the tool to organizations in the election process, such as political campaigns and media houses.
Sensity is one of the world’s leading deepfake detector solution providers, with cutting-edge technology that can detect diffusion technology used by advanced AI such as Dall-E, Stable Diffusion, FaceSwap, and Midjourney.
It detects Generative Adversarial Network (GAN) frameworks through its detection API. Sensity stands out as a dependable option for both businesses and individuals aiming to protect their digital media from the risks posed by deepfakes.
Sensity AI boasts over 95% accuracy, making it among the most reliable deepfake detectors. Additionally, it identifies text generated by Large Language Models (LLMs) like ChatGPT, even if human writers make edits to the AI-generated content.
3. Phoneme-Viseme Mismatch Detector
This game-changing scientific technique developed by Stanford University and the University of California offers a powerful solution to combat the challenges and rising threats of deepfakes.
The technique leverages the fact that deepfakes often exhibit inconsistencies between the dynamics of mouth shapes (visemes) and spoken phonemes. Beyond this, it can identify artificial facial features and spot even subtle manipulations in deepfake content.
If mismatches or discrepancies are found, the detector indicates the presence of a deepfake after analyzing the video.
With high accuracy in manual and automatic video authentication, this technique is a reliable ally in the ongoing battle against deepfake manipulation. This technology is best in academic settings, for linguistic analysis and research environments.
2. Sentinel AI
Sentinel is an Estonian cybersecurity firm established in 2019. The firm’s mission is to collaborate with governments, media outlets, and defense agencies to protect democracies against disinformation campaigns, synthetic media, and misinformation.
The platform leverages advanced neural networks to examine facial expressions, blinking patterns, and audio manipulation. It also uses natural language processing to identify irregularities in speech patterns.
Users can upload digital media for deepfake detection either directly on the Sentinel AI website or through their API.
Sentinel deepfake detection tool follows the standard of cybersecurity’s Defense in Depth (DiD), a multi-layer defense approach that makes for highly accurate deepfake detection.
1. Intel’s FakeCatcher
Intel’s Real-Time Deepfake Detector, FakeCatcher, introduced in 2022, is a pioneering tool designed to combat the proliferation of deepfakes.
Central to the system is a method known as Photoplethysmography (PPG), which, according to Intel, focuses on detecting changes in blood flow and eye movement.
These physiological signals are captured from 32 distinct regions on the face and are subsequently transformed into spatiotemporal maps.
Unlike most deep learning-based deepfake detectors, FakeCatcher relies more on authentic clues in real videos than raw data fed into the system to make a decision.
FakeCatcher is suitable for various entities that deal with large volumes of video content. These include social media platforms, news agencies, and non-profit organizations, all of which can greatly benefit from the ability to prevent the upload and dissemination of harmful deepfakes.
Intel claims that FakeCatcher wields an impressive accuracy rate of 96% and stands as a reliable tool for real-time deepfake detection.
Given the proliferation of deepfakes in the last few years, there is no better time to learn about the best deepfake detector tools than now. While these solutions vary in features and approaches, they collectively contribute to fortifying trust, security, and privacy in the face of increasing AI-driven manipulations.
As technology advances, deepfake detectors play an important role in upholding the reliability of digital content for diverse users, including governments, defense agencies, and businesses, fostering a more secure and trustworthy digital environment.