The advances in artificial intelligence and the exponential leaps in computing power have been the perfect catalyst for DeepFake technology to take off like never before.
Such tools allow for video manipulation and the creation of synthetic media. An audio track or deepfake video can also be tweaked, fueling the spread of misinformation.
For example, I saw a viral tweet that used deepfake software to create Donald Trump being arrested during an extensive trial.
A deepfake generation like this can cause much trouble for media outlets and the person in question.
As you can tell, Generative AI technology has severe repercussions for our society and can sway public opinion at crucial times.
So here comes the urgency of creating reliable DeepFake detection tools to label such fake videos and ensure the average person doesn’t fall for manipulated media.
Or if you are someone who has uploaded media before, you want to make sure you can detect signs without the need for a human eye.
Let’s explore how machine learning is used to discern real videos from fake ones and see whether our DeepFake detection solutions are reasonable today.
What Is Deepfake Detection?
DeepFake detection tools and techniques are advanced AI technology and trainable neural networks that pick on artifacts in media files and provide a confidence score to help users judge the reliability of their content.
Social media platforms like Facebook, Twitter, and TikTok have policies preventing DeepFakes from being posted online.
By analyzing Generative Adversarial Networks or GANs, the social media giants are good at detecting DeepFakes at high accuracy to avoid the spread of misinformation.
DeepFake detection technologies include video authenticator software, biological signal detectors, and a variety of forensic techniques.
Let’s examine each model's reliability in detecting DeepFakes and where improvements can be implemented to flag fake content at higher accuracy.
What Are the Best DeepFake Detection Tools?
DeepFake technology has become more accessible than ever, thanks to the availability of an open-source tool like FaceSwap.
We’ve already touched on where the DeepFake detection technology stands, so let’s explore the best software for media authentication.
- DeepWare AI: Best DeepFake Detection Software
- DuckDuckGoose: Top Deepfake detection programs for businesses
- Sensity AI: Best Deepfake detection services for anyone to use
1. DeepWare AI
Best Deepfake Detector
Since the team behind DeepWare AI started this project in 2018, the open-source tool found an active community interested in advancing DeepFake detection efforts.
DeepWare AI has access to an ever-growing library of diverse video content to ensure the detector can reliably spot synthetic media.
With over 124,000 videos, including live content, this web-based platform best uses the DeepFake Detection Challenge Dataset (DFDC.)
That’s not all, as DeepWare AI is trained on consented videos from YouTube, 4Chan, and Celeb-DF to keep up with new online trends and remain relevant in today’s ever-evolving online landscape.
Key Features
- Users can detect DeepFakes using DeepWare AI’s web platform or download the tool’s SDK for offline use.
- The AI model is trained to pick on facial manipulations, so your content must feature at least one face.
- It supports DeepFake detection in videos of up to 10 minutes in length with a detailed report.
- DeepWare AI offers an Android mobile app with an iOS version on the way.
DuckDuckGoose offers an open-source browser extension that keeps tabs on all websites you visit and alerts you once manipulated media is detected.
Users should also appreciate the transparency of the DeepFake detector, as DuckDuckGoose provides detailed explanations for why a video was flagged to give you insight into what to look for in a DeepFake.
The team behind the tool has been dedicated to sharing their research findings and encouraging participants from the community to contribute to building a more reliable model with higher accuracy.
It’s worth noting that the model is based on scalable neural network architecture, which features up to 8 facial detection algorithms that work hand-in-hand to ensure we’re living in a world with higher cybersecurity standards.
Key Features
- DuckDuckGoose detects both DeepFake videos and images with explainable insights.
- The software offers a user-friendly interface with real-time data analysis and a detailed dashboard to easily monitor any DeepFake content you encounter.
- It maintains an accuracy of over 95% with an image analysis time of under one second.
- The tool is compliant with the EU’s data privacy regulations.
3. Sensity AI
DeepFake videos and images come to life through Generative Adversarial Networks or GANs.
Those are sophisticated neural frameworks that create fake personas you might encounter online. Luckily, Sensity AI is trained to detect the latest GAN frameworks to detect DeepFakes more consistently.
The software also detects diffusion technology used by AI generators like DALL-E, MidJourney, and FaceSwap.
This is achieved with an accuracy of over 95%, making Sensity AI one of the most reliable DeepFake detectors on the market.
That’s not all, as Sensity AI also detects words generated by Large Language Models (LLMs) like ChatGPT, a collaboration between OpenAI and Microsoft.
So, even if human writers applied edits to AI-generated content, Sensity AI could still detect the use of machine learning models.
Key Features
- Sensity AI can verify the authenticity of official documents by working with more than 8,000 document templates.
- The DeepFake detection tool gives you insights into which AI framework is used to generate synthetic media and provides a confidence score for its predictions.
- Sensity AI models are trained to detect high-frequency signals that account for artifacts in fake videos.
- The company offers a web app, a cloud-based solution, and an offline SDK to give you more flexibility.
Can DeepFake Be Detected?
YES! However, accuracy might vary from one DeepFake detector to the other. It all comes down to the software’s deep learning architecture. For instance, frameworks that use the Recurrent Convolutional Strategy analyze the file’s temporal information to detect DeepFake videos.
The future for DeepFake detection is promising as tech companies invest in new technologies and encourage participants to revamp open-source APIs to detect fake videos better.
Also, we’ve seen new pixel analysis algorithms used to account for blood flow and catch upon digital face masks. It’s also worth noting that blending margin artifacts comes in handy to distinguish the form of a real human face from one created using AI.
Should We Be Worried About DeepFake Videos?
DeepFake videos can pose cybersecurity threats, especially to steering public opinion, organizing fraud attempts, and targeting phishing victims.
In addition to developing reliable tools to spot DeepFake videos, educating the public on the existence of such technology is key.
The average human isn’t trained to detect subtle artifacts in DeepFake content, especially as the technology gets exponentially better and more realistic.
When faced with over-the-top content online, you need to give the benefit of the doubt and double-check the reliability of such information before forwarding the video to all your group chats.
Why Are DeepFake Detectors Needed?
Tech companies are taking on the DeepFake detection challenge using their complex deep learning models to analyze test datasets and pick on any cues hinting at potential media file manipulation.
For instance, subtle fading artifacts at the DeepFake’s blending margin can be used to spot DeepFakes. Lip-syncing inconsistencies come in handy as well to detect fakes.
Training neural networks on enormous datasets is necessary for such models to operate accurately. This might pose an issue if synthetic media slips into such databases; that’s why companies rely on curated libraries from reputable sources.
The DeepFake detector also relies on other factors like inconsistent facial expressions, facial hair composition errors, and face recognition patterns to detect synthetic media accurately.
Phoneme-Viseme Mismatches
This following DeepFake detection technique is a mouthful; however, the concept is straightforward. Phoneme refers to the specific sounds that distinguish languages, while viseme refers to the shape of the mouth when making the same sounds.
So, any mismatch between the two gives away that such a video is created using DeepFake generation tools.
Such changes can be subtle; ordinary people won’t instantly pick upon them. Nevertheless, a sophisticated DeepFake detector with remarkable data analysis capabilities gives you a low confidence score once it detects anything iffy.
Biological Signals Detectors
Facial recognition depends on what is known as a photoplethysmography cell to outline the topography of the face and identify potentially manipulated media.
The human face is so complex that the DeepFake detector looks into 32 distinct spots to help detect the person’s identity.
Image Source: Springer
Using deepfake detection techniques, the AI model could also tell one person from another.
This could help identify whether the person in a given image is real or has a doppelgänger taking their place.
Furthermore, biological signal detectors look into facial expressions and detect computer-generated animations. The replication of human muscle movements, overlong skin distortions, and light interactions is too much to ask of a computer model.
AI detectors play the cat-and-mouse game to catch up with the advances in DeepFake technology.
There’s always a new video produced by DeepFake software that proves to be challenging for our existing AI detectors.
So don't worry, as biological signal detectors keep track of any changes in the face’s 3D animation pattern. This is made possible thanks to the use of algorithms to analyze facial landmarks in each and every frame of the video.
FAQ: DeepFake Detectors
Can Facial Recognition Detect DeepFake?
Not necessarily, as a study by Penn State University shows that facial recognition neural frameworks fall behind when presented with DeepFake videos.
If the swapped face is realistic and resembles an actual face, facial recognition modules might give it a pass.
Is DeepFake Software Illegal?
Making DeepFake videos isn’t illegal on its own. Nevertheless, the content of such videos can break any rules or regulations.
Defamation cases and copyright infringement are serious offenses, and the affected party can go to court and win a lawsuit if their likeness was used without consent.
Can You Make Money By Detecting Deepfake Videos?
You could easily make money online by offering a DeepFake detection service. Companies and individuals are often willing to pay for reliable detection services, especially when protecting their reputations or avoiding legal trouble.
As video manipulation evolves, those needing to protect their digital footprint can turn this into a profitable side hustle.
You could also develop an algorithm that can detect DeepFakes with high accuracy and make money by licensing the software or selling it as a service.
I've talked about many ways to make money with AI.
As DeepFake apps become increasingly advanced, there will be plenty of opportunities to make money by providing detection services or developing software solutions for businesses and individuals.
Bottom Line
The average human won’t give a second thought to manipulated video content, especially with all the recent advances in Deepfake video content.
It’s not all doom and gloom, though, as researchers are putting in more resources and efforts to provide reliable tools that spot DeepFake videos at high accuracy.
Choosing the suitable Deepfake detector will go a long way if required when reviewing real and fake videos.
You should do your part in standing against misinformation and verify the authenticity of online content before hitting the share button and sending it to all your group chats.
Either way, sending AI-generated videos to friends is strange. Stay safe out there.
