What are Deepfakes in Cybersecurity and what do they mean for Security?

01-Jul-2024

Deepfakes are rising at an alarming rate, posing a severe threat to society and potentially blurring the lines between reality and forgery. The sophisticated growth of artificial intelligence has resulted in the dynamic progression of synthetic media such as deepfakes. With the goal to maintain a strong cybersecurity and foster a secure digital ecosystem, man has been creating and re-inventing various methods like implementing biometrics to enforce a more trusted form of identity validation. Nevertheless, the race of technological innovations never cease.

The world today is witnessing a realistic representation of a person enabled by deepfake technology. Deepfake technology powers users to impersonate another person by replicating the physical attributes of faces, voices and irises, etc, easily. This growing sophistication is garnering concerns of cybersecurity threats, harmful manipulation and the misuse of synthetic content. Today, through this post, let us explore the intricacies of Deepfakes and throw light on the impact of deepfakes in cybersecurity. 

Understanding Deepfake and the Technology behind its power

Deepfakes are images, videos or contents created by artificial intelligence. Deepfakes are synthetic media files where an image, video, or audio of a person is doctored, manipulated and replaced with another person's face or voice. The term deepfake is derivative of two words - Deep Learning and Fake. Deepfakes technology involves the application of deep learning techniques. In deep learning, a model is powered to discover data features that enable classification or parsing and are trained at a deeper level, thus the connotation deep learning. 

Technology behind Deepfakes

Deep Learning Algorithms: Deepfakes has its foundation in deep learning algorithm, which is a part of the machine learning. In deepfakes, these deep learning algorithms assess large sets of data to learn and copy the gestures, facial expressions, and voice patterns. These elements are then merged to develop a seamless replication of the original. 

GANs (Generative Adversarial Networks) : GANs forms a critical component in Deepfake technology. It is made up of two neural networks engaged in a constant competitive cycle: i) Generator and ii) discriminator. The generator is tasked with creating fake contents and the discriminator examines its authenticity, with this constant cycle giving birth to an ever-improving quality of deepfake. 

Data acquisition and Training: The development of deepfake involves an enormous amount of datasets that includes the subject's videos, images and voice recordings. The AI model then gets trained with these data to learn and replicate the unique characteristics of the subject. The quality of the deepfake significantly depend on the availability of data.

Deepfakes in Cybersecurity Landscape and its Impact

With the skyrocketing uses and adoption of artificial intelligence, deepfakes as a cybersecurity attack vendor will evolve and become even more sophisticated. Organizations and enterprises must ensure amping up their security measures with massive and sophisticated uses of deepfakes in cybersecurity landscape. Cyber security professionals are required to be skilled in detecting and discerning impersonation of their employee, partner or customer and protect their organizational assets from harm. 

Deepfake has become a part of the phishing scam, resulting in business identity compromise. Cybercriminals may develop deepfake technology in several ways. To understand the overarching impacts deepfakes can have on cybersecurity, let us assess a few of the use cases of deepfakes.

Fraud and scams: This is one big example of deepfakes in cybersecurity. Cybercriminals can develop audio replication and use them for malicious intent like asking family or friends over phone and ask for money. There have been instances of cybercriminals creating a voice clone of a bank director asking another bank to transfer money into the criminals' account.

Identity Theft: Huge institutions like banks are at high risks with the implementation of voice biometrics for identity verification. Scammers can bypass such authentication by cloning voices. Scammers also may easily develop convincing replicas of government ID proofs to gain access to business information or a misuse it as a customer. 

Sextortion: This is another alarming concerns of deepfakes in cybersecurity. Cybercriminals employ deepfake technology to fuse pictures of prominent public figures or non-suspecting individuals with explicit or offensive images and videos and use them to extort money or to avenge them. 

Political Manipulation and Conspiracies: Cybercriminals have the capacity to make deepfakes of political and national leaders to discredit, implicate or instigate conspiracies in times of critical political situations. Such false contents may result in people ignoring real events and considering the fakes to be true, eventually giving rise to chaos and turmoil. 

Hoax: Another manipulation of deepfakes in cybersecurity is how cyber criminals uses this technology to create hoax like a political claim or warfare, instigating hatred, etc with the intent to destabilize society, or making false claims of an activity about an organization. Such crimes may prove to be an expensive affair for businesses, costing them time and money aside from the loss of reputation. 

Fake Endorsements: Inauthentic businesses today are using celebrities deepfakes for endorsing their products or services without their knowledge. This impersonation or identity theft poses risk to businesses and persons involved.  Scammers may also employ this method by using brand's name and manipulating customers, leading associated brands lose their credibility and reputation.  

What are the ways to detect Deepfakes?

Besides the technical intricacies of detecting deepfakes, there are certain subtle differences that can be spotted in Deepfakes.

• Unnatural or lack of movement in the eye
• Unnatural facial expression
• Abnormal skin colour/texture
• Unnatural hair
• Inconsistent body positioning
• Bad lip-syncing
• Unusual lighting or discolouration
• Facial Morphing
• Unnatural body structure

Deepfakes in cybersecurity is becoming a grave concern, with cyber criminals taking advantage of the increasing challenge in identifying between real or manipulated content. It is crucial to stay informed and be wary of this highly alarming rise of deepfake technology. It's crucial for organizations to keep their systems and devices updated and educate their workforce about this growing technology and its grave impact on cybersecurity. 

Today, given the criticality of deepfakes in cybersecurity, institutions have introduced a cybersecurity course covering deepfakes.  Enterprises may make their employees go through such well-structured training to combat the dangers of deepfakes. A cybersecurity course in deepfake will typically train about the technologies involved comprehensively and its implication on cybersecurity. The course will also typically explore the various methods of detecting deepfakes and combating them intelligently. 

Deepfakes will evolve into a more complicated. It's essential to stay vigilant. 

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