Artificial Intelligence and the surrounding world in it has seen many changes. The rapidly changing world is even including our everyday lives and the technology we are using. In simple language, AI enables computers to perform those tasks where human intelligence is utilized such as identifying images, making decisions, and recognizing speech.
Among many breakthroughs, AI is also widening its areas in security systems for better protection. However, among them, one stands out in disrupting our security systems and that is deepfakes. Deepfake work is to generate AI and Machine Learning for creating hyper-realistic images, videos, and audio. On the other hand, facial biometrics is a technology, whose primary work is to recognize and verify people’s identities as per their facial features. This technology is created to meet security purposes such as securing access on smartphones, airport security checks, prohibited areas, and verifying identities online.
However, with advanced technology in AI deepfakes technology brings more options to bypass the security options. This blog will explore why deepfakes AI pose a threat to security systems? What solution is available to resolve these threats? How does it ensure that our digital interactions remain safe and trustworthy?
What are Deepfakes?
The Term Deepfakes comes from “Deep Learning”, it is one type of artificial intelligence, which gained attention around 2017. It is a tool that helps a person to replace his image with any existing image or video. It uses AI technologies such as generative adversarial networks to learn from real data to produce fake images and videos. At the start, people used AI to swap faces in videos exactly from the real things. It helps companies to detect fake content
The Rise of Facial Biometric Authentication
Whether it is payment through mobile, gaining access control facial recognition is the most convenient and secure way in this digital age. All these features make Facial biometric authentication the most preferred strategy for verifying identification. As per MarketsandMarkets research report, it is estimated that by 2027 global facial recognition will reach up to $8.5 billion from $5.0 billion in 2023 at a Compound Annual Growth Rate.
How Does Deepfakes Exploit Facial Recognition?
Facial biometric systems take images to match with real-time images of a person from its stored temples. Deepfake technology fools these systems with its highly realistic facial image generation capabilities. Deepfakes can trick facial authentication by copying the user’s appearance in perfect manner.
According to California University researchers, in 85% cases, deepfakes tricked facial recognition systems when tested under controlled conditions. In such case,s it is a significant risk for organisations to completely rely on facial biometric authentication.
Technology That Works Behind DeepFakes
Generative Adversarial Networks or GANs is the technology that runs behind deepfakes. It comes in two parts, one is a generator and another a discriminator. The generator creates fake images and videos and the discriminator’s role is to tell if it is real or fake. Both the parts work are competing with each other.
As the time is progressing both are improving with their advanced technology. But the generator is improving more in advance and producing more realistic images and videos that even confuse discriminators several times. Since these systems rely on matching specific facial features, a deepfakes can replicate those features convincingly, making it nearly impossible to detect.
Real-World Examples of Deepfakes Threats
There are several instances available to describe how deepfakes can be a threat to biometric security. In one case, a deepfake video of a CEO was used to trick its subordinate into transferring $240,000 into a fraudulent account.
Researchers have created deepfake images to open the lock of a smartphone’s facial recognition system. These are the examples that describe how deepfake technology is increasing and growing day by day. If it is more accessible, instead of giving good utilization, it will be more misutilised to compromise the security system.
Highlighting Statistics for Growing Threat
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Sensity- cyber security firm in its studies says, from 2019 to 2021, the deepfakes utilisation has increased by 400%.
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According to the Norton report, deepfakes fraud makes businesses lose over $250 million by 2025.
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MIT’s research paper claims that, by 2025 the advanced deepfakes technology can easily fool facial recognition systems, which will rise to 90%.
Impact on Different Industries
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Financial Sector: The financial sectors like Banks and financial institutions mostly use facial biometric authentication to verify users’ transactions. If deepfakes can bypass this authentication, it may lead to unauthorised access to accounts.
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Government and Security: Facial recognition is utilised by many countries for national security purposes. You can have the perfect example at airports and border control areas. With Deepfakes’ fake identity one can compromise on security checks and enable people to enter into restricted areas.
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Healthcare: Deepfake utilization in the healthcare industry can breach any sensitive health data. As biometrics are used in telemedicine for patient verification, deepfakes impersonate a doctor or patient and can misuse sensitive data.
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Corporate Sector: Facial recognition system is used in companies to restrict access into sensitive areas, A deepfakes employee can access unauthorised access and can theft data or sabotage.
Why Do Current Solutions Fall Short?
Today’s biometric systems are not efficient enough to detect deepfakes. Because current systems are relying upon static images and simple video checks. The advanced level of deepfakes technology can trespass current safeguards. Easily. Some systems are making things complicated by using liveness detection such as making users blink or move their head to unlock security. But the evolving deepfakes technology seems to overcome these challenges soon.
In reply to the above evolving situation GANs is now upgrading deepfakes subtle movements where it is incorporating blinking or smiling features. Now it will be more difficult to detect these sophisticated attacks.
Is There Any Solution?
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AI-Based Detection Tools:
Upcoming AI models are developing different categories to detect deepfakes. Some are analyzing image inconsistencies, such as subtle irregularities in facial movements, and unnatural lighting setups. Such experiments need consistent updation as deepfake technology is rapidly evolving.
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Multi-Factor Authentication:
Facial recognition is no longer sufficient now onwards for security measures. Organisations need to implement multifactor authentication solutions. Such as along with facial recognition, should include fingerprint scanning, one-time password, adding more passwords codes.
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Behavioural Biometrics:
Behavioral biometric authentication can add better security features. We can include features like voice recognition or monitoring types of walks to use as another layer for security.
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Government Regulations:
Government should control deepfakes technology utilisation. So malicious uses can be checked. There should be laws to penalise those who are creating and distributing deep fakes for fraudulent purposes.
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Public Awareness:
Public should be aware about deepfakes. Everyone should be aware of the potential risks involved with this technology. As a result the public can recognise and report if find any suspicious activity.
Kodehash’s Step Towards Addressing Deepfake Threats
As a leading software solution provider, Kodehash understands the importance of facial recognition systems’ security against deepfake threats. The team at Kodehash is actively working on creating advanced AI-based authentication solutions to address deepfake detection mechanisms. The state-of-the-art algorithm with the team makes the facial recognition system resilient against emerging advanced deepfake technologies.
Kodehash’s commitment to working consistently on cyber threats to stay ahead of it,counts it among most trusted partners. As a partner, we work for organisations to make sure of protecting sensitive data and secure authentication processes.
The Future of Facial Biometric Security
Companies need to stay proactive with advanced levels of security measures. Make sure to update them on time. Because deepfakes technology is constantly evolving and challenging facial biometric technology. As the future of biometric security is relying upon the AI models to give their 100% on detecting deepfakes more effectively.
Additionally facial biometrics need to amalgamate with other authentication strategies to be more strong against deepfakes technologies. The other biometrics can be MFA or behavioural biometric authentications. Industries need to invest consistently on research and development in this field to adapt the new technologies everytime. So that they won’t compromise in front of the latest deepfakes technology.
Final Thought
AI deepfakes are becoming a headache for every industry. Day-by-day increasing advancements is making a deep area of concern for all of them who are concerning security measures. The advanced solutions with new adoptions are creating new challenges for every one. In reply to these threats, companies like Kode hash are coming forward with advanced level secure systems.
It is mandatory for companies to go for multifactor authentication if planning to stay ahead of the game. Businesses need to invest in AI-based deepfake detection and progress consistently to update their security protocols. Cyber security’s future lies in the capability of dealing with emerging threats such as facial biometric authentication, the trusted security measure.