Deepfakes in Cybersecurity: Impacts and Mitigations

What Are Deepfakes?

A deepfake is a synthetic media, usually a video or audio file, created using artificial intelligence (AI). By analysing large datasets, AI learns how a person moves, speaks, or emotes, then generates realistic fake content by superimposing their face or creating a synthetic voice. This makes it appear as though a person is doing or saying something they never did.

Deepfakes have grown more sophisticated over time, making them harder to detect with the naked eye.

Common Types of Deepfakes

• Video Deepfakes: The most notorious form of deepfakes, these alter video footage to swap faces, change speech, or create entire videos of people saying or doing things they never did.
• Audio Deepfakes: Audio deepfakes involve synthetically generated or manipulated voice recordings. Using AI models trained on a person's voice, it can create realistic fake audio that mimics their speech patterns, tone, and even emotions. This has been exploited in scams, such as fake voice messages from executives requesting wire transfers (a practice known as voice phishing).
• Image Deepfakes: These involve the manipulation of still images, such as photos, to create fake content. For example, someone’s face may be altered in a photo to make it appear that they are in a location they never visited.

Why Are Deepfakes Dangerous?

• Misinformation and Disinformation: Deepfakes can be used to spread false information, leading to political manipulation, public confusion, or even inciting violence. Fake videos or audios of public figures can quickly go viral, misinforming millions.
• Cybersecurity Threats: Cybercriminals can weaponise deepfakes for phishing, blackmail, or impersonating high-profile individuals to manipulate employees into divulging sensitive information.
• Reputational Harm: Individuals can have their reputation tarnished by deepfake content falsely portraying them in compromising situations. This is particularly concerning in the context of “deepfake pornography,” where AI-generated images place individuals in inappropriate scenarios.
• Identity Theft: Deepfakes enable criminals to mimic a person’s face or voice, gaining unauthorised access to personal, corporate, or government resources. This poses risks to financial institutions, organisations, and even national security.

What Are Some Ways to Identify Deepfakes?

They typically exhibit certain telltale signs, including:

• Inconsistencies in Facial Movements: Unnatural facial expressions, awkward blinking, or mismatched lip-syncing with the audio.
• Lighting and Shadow Mismatches: Discrepancies in shadows or lighting that don’t align with the environment or the person’s face.
• Blurring or Distortions: Blurred edges around the face or body, especially during quick movements, and distorted backgrounds.
• Audio-Video Mismatches: Audio that doesn’t sync with lip movements, or a voice that sounds robotic or lacks natural emotion.
• Inconsistent Eye Movements: Unnatural blinking patterns or eye movements that don’t match the head's position or direction.
• Stiff or Unnatural Body Movements: Jerky or rigid body movements that appear disjointed, especially if only the face is manipulated.

How To Defend Against Deepfakes?

Detecting and countering deepfakes is essential for protecting against their growing threat. Here are some key approaches:

AI-Based Detection Tools

Machine learning models and algorithms are at the forefront of deepfake detection. These systems are trained on large datasets of both authentic and fake videos, images, and audio files. They analyse videos, images, and audio to detect inconsistencies like unnatural facial movements, mismatched lip-syncing, or irregularities in lighting and shadows.

Deepfake Image and Video Analysis

Beyond AI detection tools, deepfake media can also be exposed through detailed analysis of visual and audio elements. Specialised techniques focus on identifying subtle inconsistencies that artificial generation often leaves behind. By examining media at both the pixel level and frame-by-frame, investigators can uncover signs of tampering that may not be immediately visible to the human eye.

• Pixel-Level Analysis - Such tools focus on minute details at the pixel level. Deepfakes often leave behind subtle clues, such as inconsistencies in lighting, shadows, or texture mismatches between the superimposed and original parts of an image or video. These anomalies can reveal tampered media, as pixel-level issues are hard to perfect, even with sophisticated AI.
• Frame-by-Frame Examination - Video deepfakes can often be detected through close frame-by-frame scrutiny. Tools designed for this purpose examine each individual frame for issues like blurred edges, inconsistent eye movements, and jerky transitions between facial expressions

Human Oversight and Verification

While AI tools are incredibly useful, human oversight is still essential to accurately detect deepfakes, particularly as fakes become more advanced.

• Fact-Checking and Source Verification: Human verification remains crucial in scenarios where AI might struggle to catch subtle manipulations. Verifying the authenticity of a media file by checking its origin and comparing it to verified sources can help determine if the content has been manipulated.
• Digital Watermarking: Some systems embed invisible watermarks into genuine videos and images, allowing users to confirm whether the content has been altered.

Awareness and Education

Training and education are vital in combating the rise of deepfakes, as technology alone cannot fully solve the problem.

• Training: Organisations should train employees and individuals on how to spot potential deepfakes and remain sceptical of media that seems out of character or suspiciously high-quality.
• Public Awareness Campaigns: Raising public awareness helps in reducing the spread of deepfakes and makes individuals less likely to fall victim to misinformation.

Safeguard Against the Menace of Deepfakes With Ensign

As deepfakes continue to evolve, their potential to disrupt cybersecurity grows, making deepfake detection and identification tools critical for individuals, businesses, and governments.

To combat today’s deepfakes, Ensign InfoSecurity has developed a real-time deepfake detector solution capable of detecting and determining the authenticity of synthetic or AI-augmented video. Its proprietary technology identifies manipulated media in real time, assisting both organisations and individuals in protecting against deepfake threats and preventing the dissemination of fraudulent content.

Learn more about Ensign's real-time deepfake detection solution here.