AI Lessons for Cybersecurity Teams – Deepfake

by | Jun 20, 2024 | Blog, Artificial Intelligence AI, Cybersecurity

Last September I wrote my first article about artificial intelligence (AI). I gave a few examples of how we can expect AI to help the attackers improve their craft of making our lives miserable.

In today’s article, I want to visit one of those attacks in more detail. If you like this type of information, reply to me and let me know. I’ll discuss the others in future articles.

The one I want to talk about today is Deepfake Attacks and how AI-generated deepfake content, such as videos or audio recordings, can be used to impersonate individuals, potentially leading to identity theft or misinformation campaigns.

Deepfake technology is advancing rapidly, making it increasingly challenging to distinguish between real and synthetic media, thereby necessitating continuous improvements in detection and prevention techniques.

Deepfake attacks involve creating synthetic media—typically videos or audio recordings—where a person’s likeness, voice, or actions are digitally manipulated to appear authentic. These attacks leverage advanced techniques in artificial intelligence, particularly deep learning and Generative Adversarial Networks (GANs) (discussed later), to create realistic but fake content.

Here’s a breakdown of how Deepfake attacks work:

1. Data Collection

Like all direct attacks, it starts with gathering information for the attack. Deepfake starts with collecting a large dataset of images, videos, or audio recordings of the target person. For a video deepfake, this would include many different facial expressions, angles, and lighting conditions. For audio deepfakes, a wide range of the person’s speech is required.

2. Training the model using GANs

Using this collected data, a deep learning model is trained. The most common architecture used is Generative Adversarial Networks (GANs), which consist of two parts:

  • Generator: Creates synthetic images or audio.
  • Discriminator: Evaluates the authenticity of the generated content compared to the real data.

The generator and discriminator are trained together, with the generator improving its outputs based on the discriminator’s feedback.

3. Face Swapping or Voice Cloning

Once the model is trained, it can be used to generate the deepfake’s media:

  • Face Swapping: For video deepfakes, the model maps the target person’s face onto the face of someone else in a source video. The model adjusts the target’s facial expressions, lip movements, and other features to match the source video seamlessly.
  • Voice Cloning: For audio deepfakes, the model synthesizes the target person’s voice to say things they never said, matching the speech patterns, intonation, and cadence of the target.

4. Refinement Processing

Once the media is created, it will be further refined to improve quality and realism. Such refinements include color correction and blending to ensure the face matches the skin tone, lighting, and surrounding textures, audio synchronization to align lip movements, and noise reduction to clean up any background inconsistencies.

5. Deployment

Once the media is ready, the deepfake attack can be deployed over a multitude of different venues. Standard locations would be places such as social media, websites, or messaging platforms. However, depending on the purpose of the attacker, it could also be used in unexpected areas, like tech support response, vishing (voice phishing) campaigns, text messaging (SMS), political broadcasts, or emergency contact recordings. The intent can range from humor or entertainment purposes to malicious activities like blackmail or political manipulation.


It wouldn’t be proper to discuss this attack and not provide some countermeasures for it. First, the risk is already here. Deepfake is coined because it already exists. While there is no outright way to detect Deepfake, we should all be willing to share any that we do detect. We should also expect some regulations and legal repercussions for malicious intent.

A couple of technical strategies are being developed too, including detection algorithms, which use AI to defend against deepfakes, and blockchain technology that would ensure integrity and provenance of digital media.

If you want to have a discussion on how to protect your systems more, you can schedule time with me directly.

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