In our previous blog “The Positive Impact of Deepfakes“, we discussed how deepfake technology is transforming various industries with its personalized content innovations and the positive potential this technology holds. However, since deepfakes are now nearly indistinguishable from real footage, their impact on society, politics, and individual lives can be crucial.
This technology is already causing significant concern. It is often used to impersonate individuals, leading to deception and data theft. Recently, deepfakes have increasingly been mentioned in a negative context.
In this blog, we will examine various scams involving deepfakes and how to recognize them.
How Deepfakes Work?
A deepfake is an artificial image or video generated by a special type of machine learning called deep learning. This type of machine learning feeds the algorithm examples teaching it to produce output that resembles the examples it has previously learned from. A series of nodes within the network performs mathematical transformations to convert input signals into output signals. It converts real images into compelling fake images. Neural networks are known for performing well in image recognition tasks, making their application in creating deepfakes quite straightforward.
Deepfakes use two algorithms: the generator and the discriminator. The generator is trained to produce the best possible fake replicas of real images, while the discriminator is trained to detect whether an image is fake or real. These two algorithms iterate back and forth with each one improving its task. Eventually, this results in a model that is so adept at producing fake images that people often cannot distinguish between the fake and real.
Understanding Deepfake Threats
The threat of deepfakes does not stem from the technology used to create them but from people’s tendency to believe what they see. This means that a deepfake does not have to be particularly advanced or believable to be effective in spreading misinformation. Deepfakes can have a significant impact and potentially affect various sectors, including challenges in distinguishing real from fake content, consequences in politics, security risks related to false evidence and disinformation, and concerns about personal data. We will explore some of the threats associated with deepfake technology.
Fake News
Fake news – one of the threats posed by deepfake technology, which is a form of yellow journalism or propaganda created to spread deliberate misinformation or false news using online social networks or traditional print media.
When fake news is combined with deepfake technology, this threat becomes even more powerful and dangerous. This problem has become global in recent years because it influences people’s actions, either consciously or subconsciously. Some of the impacts of deepfake-driven fake news include the loss of trust, the spread of misinformation, damage to the reputation of public figures, and similar consequences.
Deepfake Scams
Deepfake scams use this technology to create scams that destabilize organizations. There are many examples of deepfake scams.
One such example is when cybercriminals create fake videos in which the head of an organization appears to admit to criminal activities or makes false claims about the organization’s actions. The goal is to harm the organization by affecting its public reputation and share price. Additionally, such misrepresentations may lead to unauthorized transactions, leaks of confidential information, disruptions in decision-making, and false announcements or statements.
Social Engineering
Deepfake technology is used in social engineering scams too. Such scams have been feared by companies worldwide because they threaten businesses by using artificial intelligence to replace faces, create new identities, manipulate voices, or generate completely fictional content that is increasingly difficult to distinguish from reality.
An example of such scams involves attackers using AI-based voice and video cloning technology to trick potential brands into making corporate fund transfers. Security experts claim that deepfake technology is rapidly becoming a major component of spear-phishing and social engineering attacks.
Identity Theft
Deepfakes can also be used to create new identities or steal the identities of real people. Attackers aim to create accounts or purchase products by pretending to be the person whose identity they have stolen, often by creating fake documents or spoofing the victim’s voice.
Cybercriminals who engage in these activities collect data for identity theft in various ways; for example, they hack devices that use biometrics, especially facial recognition, or they harvest audio, video, and photo samples from social networks. The target of these thefts is usually financial resources, as attackers often carry out these scams to request fund transfers from the victim’s family or gain access to their accounts or account numbers.
How to Spot Deepfakes?
In its early stages, deepfake technology was far from perfect and often left signs that indicated manipulation. Since some errors were very noticeable, it was easy to distinguish a fake video from a real one. However, as technology has advanced, it has become increasingly difficult to recognize a deepfake video, so you need to look more closely and pay attention to certain signs.
Pay attention to the face
Deepfake manipulations most commonly involve the face, so it is important to carefully observe the edges of the face. Do they match the rest of the head or body? Are they sharp or blurry?
Pay attention to blinking
With deepfake technology, a person may blink too much or too little.
Pay attention to lip movements
Many deepfakes rely on lip-syncing, so if the lips don’t move naturally, it can be a sign of manipulation.
Pay attention to the face and body
One way to detect forgery is to identify discrepancies between the proportions of the face and the body, or between facial expressions and body movements or positions.
Other details
Details are the weak point of deepfake technology, which is why you can spot defects when you notice blurry shadows around the eyes, unrealistic facial hair, overly smooth or wrinkled skin, fake moles, unnatural lip color, blurred teeth, and other irregularities.
Conclusion
Deepfake technology has impacted voice, image, and video, opening up a landscape of new possibilities but also significant risks. Deepfake videos are increasingly targeting public figures, resulting in reputational damage or loss of credibility. As consumers continue to rely on digital multimedia content, the risks associated with deepfake technology are rising significantly.
The threats are becoming more sophisticated, so individuals and organizations must prioritize proactive measures, starting with the ability to detect deepfake scams. Since this is still a developing technology, there are no foolproof solutions. The safest approach is to strengthen personal security practices and improve employee awareness. That can significantly reduce the risks associated with deepfake technology.