What is DeepFake AI? It’s more than just a buzzword; it’s a transformative force that redefines our perception of reality. DeepFake has come to light after several incidents of computer-generated hyperrealistic forgeries in recent times. Threat actors use DeepFake technology to spread disinformation through video hoaxes, images, and audio. This blog will explore DeepFake technology, its working, preventive measures, and more.
What is DeepFake AI?
Deepfake AI uses artificial intelligence (AI) to craft convincing images, audio, and video deceptions. It involves transforming existing content by swapping individuals or generating entirely new content, portraying individuals doing or saying things they never did. The term “DeepFake” combines deep learning and fake.
The primary risk of deepfakes lies in their ability to spread false information, often mimicking trusted sources. Concerns have also been raised over the potential election interference and propaganda.
Despite their risks, deepfakes have legitimate applications, such as video game audio, entertainment, and various customer support applications like call forwarding and receptionist services.
How does DeepFake Work?
Deepfake technology uses two key algorithms, a generator, and a discriminator, to create and refine fake content. The generator develops fake digital content based on the desired output, while the discriminator determines its realism. The generator enhances its ability to create convincing content through repetitive iterations while the discriminator becomes more skilled at identifying flaws for correction.
This combination of generator and discriminator forms a generative adversarial network (GAN). GANs use deep learning to recognize patterns in real images, using them to create fakes. In deepfake photography, GAN systems study target photos from various angles for comprehensive detail capture. In deepfake videos, GANs view the video from different angles, considering behavior, movement, and speech patterns. Multiple passes through the discriminator fine-tune the realism of the final image or video.
Deepfake videos emerge through two methods: altering an original video source of the target or swapping the person’s face onto another individual’s video, known as a face swap. A few specific approaches to creating DeepFakes include:
- Source video DeepFakes involve a neural network-based autoencoder that analyses a video’s content, understanding attributes like facial expressions and body language. The encoder encodes these traits, and the decoder then imposes them onto the original video.
- Audio DeepFakes: It uses a GAN to clone a person’s voice, creating a model based on vocal patterns for customized speech. Video game developers often use this.
- Lip syncing: In DeepFakes, a voice recording is mapped to a video, creating the illusion that the person in the video is speaking the recorded words. This technique is enhanced by recurrent neural networks, adding an extra layer of deception when the audio itself is a deepfake.
Key Technologies Shaping DeepFake Technology
The evolution of deepfake technology is marked by advancements in the following key areas, facilitating more accessible, more accurate, and more prevalent development:
- GAN Neural Networks: GAN technology, using generator and discriminator algorithms, underpins the development of all deepfake content.
- Convolutional Neural Networks (CNNs): Used for facial recognition and movement tracking, CNNs analyze patterns in visual data, enhancing the accuracy of deepfake generation.
- Autoencoders: These neural networks identify pertinent attributes, such as facial expressions and body movements of a target, subsequently imposing these features onto the source video during deepfake development.
- Natural Language Processing (NLP): NLP algorithms are crucial in crafting DeepFake audio. These algorithms generate original text with matching characteristics by analyzing attributes of a target’s speech.
- High-Performance Computing: It is a form of computing that offers the vital computational power needed by DeepFakes.
Examples of Deepfake Applications and Implications
The use of DeepFakes spans diverse realms, with primary applications including:
- Art: DeepFakes produce music by leveraging the existing body of an artist’s work.
- Customer Phone Support: Fake voices are used for routine tasks like checking account balances or filing complaints in customer support services.
- Misinformation and Political Manipulation: DeepFake videos of politicians or trusted figures influence public opinion, creating confusion in various contexts, as seen in the DeepFake of Ukrainian President Volodymyr Zelenskyy case.
Celebrities are the easiest victims for these threat actors and scammers. Recently, the former chairman of Tata Group, Ratan Tata, and film stars Priyanka Chopra and Rashmika Mandhana were targeted. A fake video of Ratan Tata giving investment advice started doing the rounds on Instagram. In another viral DeepFake video, scammers replaced Priyanka Chopra’s voice with someone else’s in a brand endorsement advertisement.
- Blackmail and Reputation Damage: Examples include manipulating target images into compromising situations, leading to extortion, reputation damage, revenge, or cyberbullying. Nonconsensual DeepFake porn, also known as revenge porn, is a prevalent form of blackmail.
- Stock Manipulation: Forged DeepFake materials can influence a company’s stock price by disseminating misleading information, affecting investor perceptions.
- Fraud: Impersonation is a significant application where DeepFakes are used to mimic individuals to obtain personally identifiable information, posing cybersecurity threats.
- Caller Response Services: DeepFakes play a role in personalized responses to caller requests, particularly in call forwarding and receptionist services.
- Texting: The U.S. Department of Homeland Security anticipates using DeepFake technology in text messaging, allowing threat actors to replicate a user’s texting style for malicious purposes.
Insight into Techniques to Identify DeepFake Events
Detecting DeepFake attacks involves adhering to several best practices. Potential signs of deepfake content include:
- Unusual Facial Positioning
- Unnatural Facial or Body Movement
- Unnatural Coloring
- Odd Appearance when Zoomed In
- Inconsistent Audio
- Lack of Blinking
For textual deepfakes, indicators may include:
- Unnatural Flow of Sentences
- Suspicious Source Email Addresses
- Mismatched Phrasing
- Out-of-Context Messages
It’s crucial to note that advancements in AI are gradually overcoming some of these indicators, with tools supporting natural blinking, highlighting the dynamic nature of the deepfake landscape.
How to Defend Against DeepFakes
Companies, organizations, and government agencies like the U.S. Department of Defense’s Defense Advanced Research Projects Agency are actively developing technology to detect and block deepfakes. Some social media platforms use blockchain technology to authenticate the origin of videos and images, ensuring only content from trusted sources is allowed. Consequently, both Facebook and Twitter have implemented bans on malicious deepfakes.
The following leading companies provide deepfake protection solutions:
- Adobe: Offers a system enabling creators to attach a signature to videos and photos, providing detailed information about their creation.
- Microsoft: Uses AI-powered DeepFake detection software that analyzes videos and photos to provide a confidence score indicating whether the media has been manipulated.
- Operation Minerva: Uses catalogs of previously identified deepfakes to discern if a new video is a modification of an existing fake that has been discovered and assigned a digital fingerprint.
- Sensity: Provides a detection platform leveraging deep learning to identify indications of DeepFake media, akin to how antimalware tools detect virus and malware signatures. Users receive email alerts when encountering a DeepFake.
While the rise of DeepFake technology presents several challenges and potential risks, the ongoing efforts in detection and prevention underscore the commitment of various companies, organizations, and government agencies to safeguard against malicious intent. As we navigate this evolving landscape, we must remain vigilant, using advanced tools and technologies to distinguish reality from deception.