Artificial intelligence (AI)-generated fraudulent videos that can easily deceive average viewers have become commonplace as modern computers have enhanced their ability to simulate reality.
For example, modern cinema relies heavily on computer-generated sets, scenery, people, and even visual effects. These digital locations and props have replaced physical ones, and the scenes are almost indistinguishable from reality. Deepfakes, one of the most recent trends in computer imagery, are created by programming AI to make one person look like another in a recorded video.
What is a deepfake?
Deepfakes resemble digital magic tricks. They use computers to create fraudulent videos or audio that appear and sound authentic. It's like filming a movie, but with real people doing things they've never done before.
Deepfake technology relies on a complicated interaction of two fundamental algorithms: a generator and a discriminator. These algorithms collaborate within a framework called a generative adversarial network (GAN), which uses deep learning concepts to create and refine fake content.
Generator algorithm: The generator's principal function is to create initial fake digital content, such as audio, photos, or videos. The generator's goal is to replicate the target person's appearance, voice, or feelings as closely as possible.
Discriminator algorithm: The discriminator then examines the generator's content to determine if it appears genuine or fake.
The feedback loop between the generator and discriminator is repeated several times, resulting in a continual cycle of improvement.
Why do deepfakes cause concerns?
Misinformation and disinformation: Deepfakes can be used to make convincing films or audio recordings of people saying or doing things they did not do. This creates a significant risk of spreading misleading information, causing reputational damage and influencing public opinion.
Privacy invasion: Deepfake technology has the ability to violate innocent people's privacy by manipulating their images or voices for malicious intents, resulting in harassment, blackmail, or even exploitation.
Crime and fraud: Criminals can employ deepfake technology to imitate others in fraudulent operations, making it challenging for authorities to detect and prosecute those responsible.
Cybersecurity: As deepfake technology progresses, it may become more difficult to detect and prevent cyberattacks based on modified video or audio recordings.
How to detect deepfakes
Though recent advances in generative Artificial Intelligence (AI) have increased the quality of deepfakes, we can still identify telltale signals that differentiate a fake video from an original.
- Pay close attention to the video's commencement. For example, many viewers overlooked the fact that the individual's face was still Zara Patel at the start of the viral Mandana film; the deepfake software was not activated until the person boarded the lift.
- Pay close attention to the person's facial expression throughout the video. Throughout a discourse or an act, there will be irregular variations in expression.
- Look for lip synchronisation issues. There will be some minor audio/visual sync issues in the deepfake video. Always try to watch viral videos several times before deciding whether they are a deepfake or not.
In addition to tools, government agencies and tech companies should collaborate to develop cross-platform detection tools that will stop the creation of deepfake videos.
