The phrase “deepfake”, an amalgamation of the words “deep learning” and “fake,” is defined as any method of synthetic media, images, or video, that is manipulated in order to create a piece of media that conveys a different message. Using machine learning algorithms, malicious actors compile images and sounds from various sources, creating hoax videos and/or pictures. Made up of neural networks, a type of machine learning technology, deepfakes are most commonly used to mislead the public by spreading misinformation or propaganda, typically with the ultimate goal of swaying public opinion on a person or idea. As deepfakes pose a major risk to our cybersecurity, Netizen has created an overview of the security threats the emerging technology poses, an analysis of the technology itself, and an advisory on how to deal with deepfakes.
Deepfake Technology as a Cybersecurity Threat
Emerging technology can and always will be utilized by threat actors to gain a foothold in systems and overall achieve various other malicious goals. Here are some security threats associated with the emergence of deepfake technology:
- Social Engineering: Deepfakes significantly enhance the effectiveness of social engineering attacks by providing visual or auditory proof, making deceptive claims more believable. For example, a deepfake video could be created to show a fake endorsement from a trusted figure, thereby tricking individuals into taking actions such as clicking on malicious links or providing sensitive information.
- Difficulty of Authentication: The sophistication of deepfakes creates a challenge in authenticating digital media. Traditional methods of verifying the authenticity of images or videos may become obsolete, necessitating the development of advanced detection and verification technologies. This raises the bar for cybersecurity measures and could lead to increased operational costs for organizations.
- Misinformation Campaigns: Deepfakes can be used to fabricate realistic-looking media to spread misinformation, stir public discord, or manipulate opinions on a large scale. They can be deployed to create fake news, alter public perception, or even influence elections and other significant events. The potential for deepfakes to spread virally on social media platforms amplifies the risks associated with misinformation campaigns.
- Identity Theft: By creating realistic representations of individuals saying or doing things they never did, deepfakes enable a new form of identity theft. This could be used for fraud, defamation, or to cause reputational damage. For instance, malicious actors could create deepfake videos of executives making false statements to manipulate stock prices or deceive stakeholders.
In order to be a part of the threats above, deepfake technology utilizes a subset of machine learning known as “deep learning,” comprised of neural networks, to synthesize the media it creates.
What is a Neural Network?
A neural network is a computing model inspired by the human brain’s interconnected neuron structure, designed to analyze and interpret data. Basic neural networks are comprised of three layers that each have a different role in processing.
- Input Layer: The layer where the network receives its data. Each neuron in this layer corresponds to one element in the input data.
- Hidden Layers: These are the layers between the input and output layers, where the computation happens. They help in extracting and refining input data.
- Output Layer: This is where the network makes a decision or prediction based on the input data and the computations that have occurred in the hidden layers.
Deep neural networks are more complicated than typical neural networks, they’re comprised of more than 3 layers to ensure higher degrees of accuracy in synthesis. Deep learning networks are capable of automatically discovering, learning, and extracting features from data without any manual feature engineering, which is what makes them highly effective for various complex tasks, for example creating a realistic deepfake of a celebrity or political candidate.
Different Types of Neural Networks in Deepfake Technology
Due to the complexity of deep learning, many different types of neural networks are behind the creation of deepfake visual media.
- Generative Adversarial Networks (GANs): Deepfakes primarily employ Generative Adversarial Networks (GANs), a class of machine learning frameworks, where two neural networks, namely the Generator and the Discriminator, contest with each other. The Generator creates new data instances that resemble a given dataset, while the Discriminator evaluates the generated data, distinguishing between real and fake. Through iterative training, the Generator becomes proficient at producing realistic data, capable of fooling the Discriminator.
- Autoencoders and Variational Autoencoders (VAEs): Besides GANs, autoencoders, especially Variational Autoencoders (VAEs), are used to decompose and reconstruct images, enabling the modification of facial features in videos. These networks learn to encode the data into a lower-dimensional space and decode it back to its original form, with modifications as required to create deepfakes.
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM): For audio synthesis or manipulation, technologies like Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs) are employed. They are adept at handling sequential data, making them suitable for audio and video processing.
All 3 types of neural networks are utilized heavily in deep learning, and through deep learning deepfake technology. By utilizing these different neural networks in a larger artificial intelligence model, threat actors can create extremely convincing photos, audio, and video for malicious purposes.
Preventing deepfakes from affecting personal and organizational cybersecurity is a multi-step process that requires both continuous effort and awareness. Here are several advisory steps that can be taken to ensure you’re protected:
- Education and Awareness: Raise awareness about deepfakes among employees and stakeholders. Conduct training sessions to educate them on how deepfakes can be used maliciously, and how to recognize potential deepfake attempts.
- Verification Procedures: Implement robust verification procedures for sensitive communications. For instance, use multi-factor authentication, and confirm requests for sensitive information or transactions through a secondary communication channel.
- Deepfake Detection Tools: Invest in or develop deepfake detection tools that can analyze digital media to determine its authenticity. Keep these tools updated as deepfake technology evolves.
- Regular Audits: Conduct regular audits to check the integrity of digital media and communications within your organization.
- Secure Communication Channels: Employ secure communication channels with end-to-end encryption to ensure that the data being shared remains confidential and unaltered.
- Cybersecurity Policies: Update your cybersecurity policies to address the risks posed by deepfakes. This includes defining procedures for verifying and handling digital media, especially in critical or sensitive situations.
- Incident Response Plan: Develop an incident response plan for handling potential deepfake attacks. This plan should outline how to verify the authenticity of suspicious communications, how to contain and mitigate the impact of a deepfake attack, and how to communicate with stakeholders during and after an incident.
- Continuous Monitoring: Establish a continuous monitoring system to detect unusual activities, and keep abreast of the latest developments in deepfake technology and detection techniques.
By taking a comprehensive approach that combines education, technical solutions, and proactive monitoring, you can significantly mitigate the risks posed by deepfakes to your or your organization’s cybersecurity.
How Can Netizen Help?
Netizen ensures that security gets built-in and not bolted-on. Providing advanced solutions to protect critical IT infrastructure such as the popular “CISO-as-a-Service” wherein companies can leverage the expertise of executive-level cybersecurity professionals without having to bear the cost of employing them full time.
We also offer compliance support, vulnerability assessments, penetration testing, and more security-related services for businesses of any size and type.
Additionally, Netizen offers an automated and affordable assessment tool that continuously scans systems, websites, applications, and networks to uncover issues. Vulnerability data is then securely analyzed and presented through an easy-to-interpret dashboard to yield actionable risk and compliance information for audiences ranging from IT professionals to executive managers.
Netizen is an ISO 27001:2013 (Information Security Management), ISO 9001:2015, and CMMI V 2.0 Level 3 certified company. We are a proud Service-Disabled Veteran-Owned Small Business that is recognized by the U.S. Department of Labor for hiring and retention of military veterans.
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