AI Deepfake Security Concerns

Ken Huang is the CEO of DistributedApps.ai and Co-Chair of the CSA AI Organizational Responsibilities and AI Controls Working Groups. Huang is an acclaimed author of eight books on AI and Web3, a core contributor to OWASP's Top 10 Risks for LLM Applications, and heavily involved in the NIST Generative AI Public Working Group. In the following interview, Huang zeroes in on the critical topic of AI deepfakes and why IT professionals should be deeply invested in learning more.

What is your experience with AI?

My first experience with AI was as a visiting scholar at the University of Lausanne in the 1990s. My first research paper was published in 1995 by Springer on the intelligent tutoring system based on a rule-based declarative expert system with Professor Pierre Bonzon. I later used different AI/ML models such as decision tree, SVM, and BERT in different research or work-related projects. This experience led me to study GPT2 before the ChatGPT moment in November 2022. My experience and my focus on cybersecurity aspects of advanced technology allowed me to contribute to various books on the security aspects of AI, Quantum, and Web3.

What is one of your top concerns regarding AI and security?

The topics surrounding generative AI security are vast. For this blog, I would like to focus on just one: Deepfake and Identity Management.

How would you describe deepfakes from your point of view?

Here is why deepfakes are a critical and urgent topic from a CIA (Confidentiality, Integrity, Availability) perspective, identity management perspective, and AI safety perspective:

CIA Perspective

The rapid advancement of deepfake technology poses grave concerns for information security and the trustworthiness of digital media. Deepfakes, which are synthetic media generated using AI algorithms, can create highly realistic but fake video and audio content of people saying and doing things they never actually said or did. This technology is becoming increasingly accessible and convincing, enabling a wide range of malicious uses that threaten the confidentiality, integrity, and availability of information in our increasingly digital world. Let's explore each of these risks in more detail:

1. Confidentiality: Deepfakes can pose a serious threat to confidentiality by enabling bad actors to impersonate individuals in order to gain unauthorized access to sensitive information or systems.

For example, a deepfake video could be created showing a company executive or government official saying things they never actually said. This fake video could then be used as a form of social engineering to trick people into revealing confidential data, passwords, etc. by making the impersonated individual appear to authorize the access. Deepfaked audio could potentially even be used to bypass voice authentication systems. The ability to generate fake but convincing video and audio of people creates new attack vectors to compromise confidentiality.

2. Integrity: The use of deepfakes can severely undermine the integrity of digital content by making it easy to manipulate or fully fabricate audiovisual "evidence."

For instance, deepfakes could be used to create fake videos showing someone committing a crime they didn't actually commit, or saying inflammatory/problematic things they never actually said. If it becomes trivial for anyone to generate fake content that most people cannot easily distinguish from real content, it will erode trust in all media and make it much harder to use audiovisual evidence (e.g. in journalism, criminal investigations, courtrooms, etc.). We may enter a "post-truth" world where even real footage can be dismissed as a deepfake, severely damaging the integrity of information.

3. Availability: As deepfakes lower the cost and effort required to generate fake content, we may see an explosion of misinformation and disinformation powered by deepfake technology. If bad actors can easily churn out massive volumes of fake videos spreading false propaganda, conspiracy theories, and other misleading content, it could flood the information ecosystem with noise and drown out reliable information.

For example, in a political context, a malicious group could generate thousands of deepfake videos spreading lies about a candidate to overwhelm fact-checkers and confuse voters. This deluge of synthetic misinformation could make it increasingly difficult for people to access and identify truthful, accurate information, reducing the general availability of reliable information.

Secure Identity Management Perspective

From a secure identity management perspective, deepfakes pose significant challenges to identity provisioning and authentication processes, particularly those that rely on biometric information. Here's why deepfakes are an urgent concern in this context:

Identity Provisioning

1. Remote Identity Proofing: Many identity provisioning systems rely on remote identity proofing, which often involves the use of biometric information, such as facial recognition or voice verification. Deepfakes can be used to create highly realistic fake biometric data, making it difficult for these systems to distinguish between genuine and fraudulent identities.

2. Legacy Code Vulnerabilities: Legacy code for identity provisioning may not have been designed with deepfake threats in mind. As a result, these systems may be vulnerable to attacks that use deepfakes to bypass security measures and create fake identities.

3. Onboarding Processes: Deepfakes can be used to manipulate or fabricate identity documents, such as passports or driver's licenses, which are often required during onboarding processes. This can lead to the creation of fraudulent accounts or the hijacking of existing ones.

Authentication

1. Biometric Authentication: Many authentication systems rely on biometric information, such as facial recognition, voice recognition, or fingerprint scanning. Deepfakes can be used to create fake biometric data that can fool these systems, allowing unauthorized access to sensitive information or systems.

2. Multi-Factor Authentication (MFA): Even if an authentication system uses MFA, deepfakes can still pose a threat. For example, a deepfake video could be used to fool a liveness detection check during a facial recognition process, or a deepfake voice could be used to bypass voice-based authentication.

3. Continuous Authentication: Some authentication systems use continuous biometric monitoring to ensure that the user remains the same throughout a session. Deepfakes could be used to create a consistent fake biometric profile, allowing an attacker to maintain unauthorized access.

Mitigation Strategy

To mitigate the risks posed by deepfakes, identity management systems need to:

1. Implement Robust Deepfake Detection Mechanisms: This may involve the use of AI-based algorithms that can identify the subtle artifacts and inconsistencies present in deepfakes.

2. Strengthen Liveness Detection: Liveness detection methods, such as requiring users to perform specific actions or analyzing micro-expressions, can help distinguish between real and fake biometric data.

3. Use Multimodal Biometrics: Combining multiple biometric modalities, such as facial recognition and voice recognition, can make it more difficult for deepfakes to fool the system.

4. Regularly Update and Patch Legacy Code: Legacy code for identity provisioning and authentication should be regularly reviewed, updated, and patched to address emerging deepfake threats.

5. Implement Secure Identity Proofing Processes: Identity proofing processes should incorporate additional security measures, such as document verification and cross-referencing with trusted data sources, to reduce the risk of deepfake-based fraud.

By addressing the threats posed by deepfakes, organizations can strengthen their identity management systems and protect against fraudulent activities that could compromise sensitive information and systems.

AI Safety Perspective

From an AI safety perspective, the emergence of deepfake technology raises a number of grave concerns about the potential for harm to individuals, society, and democratic institutions. As AI systems become more advanced and accessible, the risk of these powerful tools being misused or abused grows, highlighting the urgent need for responsible development practices and robust safety measures. The following points examine some of the key AI safety issues surrounding deepfakes:

1. Election Interference: One of the most alarming potential applications of deepfakes is in the realm of election interference and political manipulation. Malicious actors could use deepfake technology to create convincing fake news articles, videos, or audio clips of candidates saying or doing controversial or damaging things. These synthetic media could be strategically deployed to manipulate public opinion, sway voter preferences, or even suppress turnout by sowing confusion and doubt. The use of deepfakes to interfere in democratic processes poses a serious threat to the integrity of elections and the stability of political systems.

2. Ethical Concerns: The creation and dissemination of deepfakes raises significant ethical questions about consent, privacy, and the potential for harm. Deepfakes can be used to create non-consensual pornography, violating individuals' privacy and autonomy. They can also be used to harass, bully, or intimidate people by putting their likeness into humiliating or distressing situations. The ease with which deepfakes can be created and spread also raises concerns about the erosion of individual control over one's own image and reputation. The ethical implications of this technology warrant serious consideration.

3. Erosion of Trust: As deepfakes become more sophisticated and harder to detect, they have the potential to erode trust in media, institutions, and even interpersonal relationships. If it becomes impossible to distinguish genuine audiovisual content from synthetic content, people may lose faith in the authenticity of all media. This erosion of trust could undermine journalism, hamper the use of video evidence in legal proceedings, and sow widespread doubt and confusion. The loss of a shared basis for truth and reality could have deeply destabilizing effects on society.

4. Misuse of AI: The development of deepfakes demonstrates the potential for AI technologies to be misused or abused for harmful purposes. As AI systems become more powerful and easier to use, the risk of malicious applications increases. Deepfakes highlight the need for AI researchers and developers to prioritize safety and security considerations, implement robust safeguards against misuse, and proactively work to mitigate potential harms. The responsible development of AI is crucial to ensuring that these technologies benefit society rather than causing damage.

5. Identity Theft and Fraud: Deepfakes can be used to create fake identities or impersonate real individuals, facilitating various forms of identity theft and fraud. For example, synthetic media could be used to create fake social media profiles, generate false evidence to support scams or phishing attempts, or even bypass biometric authentication systems. The ability to assume someone else's likeness with high fidelity could enable a range of criminal activities and security breaches.

6. Psychological and Emotional Harm: Beyond the risks to institutions and systems, deepfakes can also inflict serious psychological and emotional harm on individuals. The non-consensual creation of intimate or pornographic deepfakes is a form of sexual violence that can cause deep trauma and distress to victims. Deepfake harassment campaigns can also cause severe emotional anguish, reputational damage, and professional harm. The potential for deepfakes to be weaponized against individuals is a grave concern from both an AI safety and human rights perspective.

7. Disinformation and Propaganda: Deepfakes could be a powerful tool for spreading disinformation and propaganda at an unprecedented scale. By creating vast amounts of synthetic media pushing particular narratives, malicious actors could flood the information ecosystem with false content, drowning out truthful information. This could have a deeply corrosive effect on public discourse, making it harder for people to discern fact from fiction and form evidence-based opinions. The mass deployment of deepfakes for disinformation could overwhelm our collective capacity for critical thinking and informed decision-making.

Why should IT and security professionals be invested in learning about deepfakes?

IT and security professionals should be deeply invested in learning about deepfakes because this emerging technology poses significant risks to the security, integrity, and trustworthiness of digital systems and information. As the guardians of an organization's technology infrastructure and data, IT and security professionals need to be at the forefront of understanding and mitigating the potential threats posed by deepfakes. Here are some key reasons why:

1. Cybersecurity Threats: Deepfakes can be used as a tool for social engineering attacks, phishing scams, and other cybersecurity threats. For example, a deepfake video or audio clip of a company executive could be used to trick employees into transferring funds, revealing sensitive information, or granting access to secure systems. IT and security professionals need to be aware of these emerging attack vectors to develop appropriate defenses and incident response plans.

2. Fraud Detection: As deepfakes become more sophisticated, they may be used to commit various types of fraud, such as creating fake identities, forging documents, or manipulating financial records. IT and security professionals will need to adapt fraud detection systems and processes to account for the possibility of deepfake-based fraud.

3. Data Integrity: Deepfakes can be used to manipulate or falsify data, undermining the integrity of an organization's information assets. IT and security professionals need to understand how deepfakes could be used to corrupt databases, alter logs, or generate false data in order to implement appropriate safeguards and maintain the reliability of data.

4. Reputational Risk: Deepfakes targeting an organization, its employees, or its customers could cause serious reputational damage. IT and security professionals need to be prepared to quickly detect and respond to deepfake-based attacks on an organization's reputation, such as fake videos or audio recordings purporting to show company representatives engaging in unethical or illegal behavior.

5. Authenticity and Trust: As deepfakes erode trust in digital media, IT and security professionals will play a crucial role in developing and implementing technologies and processes to authenticate genuine media and detect synthetic content. This may involve deploying deepfake detection algorithms, blockchain-based authentication systems, or other innovative solutions to ensure the authenticity of an organization's media assets.

6. Informed Decision-Making: IT and security professionals need to stay informed about the latest developments in deepfake technology in order to make sound decisions about an organization's technology investments, security strategies, and risk management approaches. A deep understanding of the capabilities and limitations of deepfakes will be essential for navigating the complex technical and ethical challenges posed by this technology.

7. Thought Leadership: As experts in the field, IT and security professionals have a responsibility to educate other stakeholders within their organizations about the risks and implications of deepfakes. By taking a proactive stance and sharing their knowledge, IT and security professionals can help build organizational resilience and foster a culture of informed vigilance.

What is a potential AI use case or development that you’re excited about?

I have to say that as a cybersecurity professional with deep expertise in generative AI, and chief editor of “Generative AI Security,” one potential use of generative AI is in cyber defense.

Generative AI can be leveraged to create advanced threat detection systems that can identify and mitigate cyber threats in real-time. By analyzing vast amounts of data, these AI systems can recognize patterns and anomalies that may indicate a security breach, allowing for quicker and more effective responses.

Moreover, generative AI can be used to simulate potential cyber attacks, providing cybersecurity teams with valuable insights into how these attacks might unfold and how best to defend against them. This proactive approach can help organizations stay one step ahead of cybercriminals, continuously improving their security measures based on the latest threat intelligence.

Additionally, generative AI can assist in automating routine security tasks, such as monitoring network traffic, analyzing logs, and managing security alerts. This automation not only enhances efficiency but also frees up human analysts to focus on more complex and strategic aspects of cybersecurity.

Furthermore, generative AI can be employed to develop sophisticated phishing detection systems. By generating and analyzing potential phishing emails, these systems can better understand the tactics used by cybercriminals and improve their ability to detect and block such threats before they reach end-users.

There are already many generative AI-enabled cybersecurity tools which can help cybersecurity professionals in their daily work and I expect the innovation in this space to speed up in the near future.

Check out CSA’s AI Safety Initiative to learn more about safe AI usage and adoption.