The Threat of Deepfakes and How to Unmask Them in Time

What Is a Deepfake?

A deepfake is a form of synthetic media created using artificial intelligence (AI)—specifically, deep learning models—to mimic or fabricate realistic visuals and sounds. The term itself is a combination of “deep learning” (a subset of machine learning) and “fake”, indicating its artificial nature.

Deepfakes are primarily used to replicate or simulate people—their faces, voices, and behaviors—in a way that can deceive both humans and machines.

1. Make Someone Appear to Say or Do Things They Never Did

Deepfake video technology can edit existing footage or create entirely new clips to make a person appear to:

• Say words they never spoke

• Perform actions they never actually did

• Appear in places they never visited

Example: A deepfake can generate a video of a politician giving a false speech, potentially misleading the public or causing political unrest. The result may look so convincing that only AI-powered forensic tools can detect the forgery.

These videos are often used in:

• Disinformation campaigns

• Political manipulation

• Scandal creation or defamation

2. Clone Voices with Just a Short Sample of Audio

Deepfake voice technology (also known as voice cloning) can mimic someone’s tone, accent, and manner of speaking with high precision using as little as 10 to 30 seconds of recorded speech.

The cloned voice can then be used to:

• Make phone calls

• Create voice messages

• Power chatbots or impersonation scams

Example: Attackers cloned a CFO’s voice to instruct a company employee to transfer millions of dollars. The victim complied, believing the voice was authentic.

These attacks fall under vishing (voice phishing) and are increasingly common in:

• Business Email Compromise (BEC) scams

• Fraudulent bank transactions

• Impersonation of family members or executives

3. Generate Realistic Photos of People Who Don’t Exist

Using AI models like StyleGAN, deepfake technology can create entirely fictitious human faces that:

• Look real but belong to no actual person

• Include details like hair texture, eye shape, skin tone, lighting, and even reflections

These images are used in:

• Fake social media profiles

• Online scams or phishing accounts

• Bot-driven marketing and misinformation campaigns

Example: A fake LinkedIn profile of a “cybersecurity expert” with a convincing AI-generated photo was used to connect with real professionals, extract company information, and spread malicious links.

How Is This Done?

The core technology behind deepfakes includes:

GANs (Generative Adversarial Networks)

A type of AI where two neural networks - a generator and a discriminator - compete against each other:

• The generator tries to create realistic fake content.

• The discriminator tries to detect if it’s fake. Over time, the generator improves so much that its outputs become almost indistinguishable from real media.

Used for:

• Face swapping

• Video manipulation

• Photo generation

Voice Cloning Models

These models, such as Tacotron 2, Descript’s Overdub, or Resemble.ai, analyze the frequency, rhythm, and tone of a person’s voice to reproduce it synthetically.

They can be fine-tuned with minimal training data, making them dangerous in the wrong hands.

What Is a Deepfake?

Where and Why Deepfakes Are Used

Deepfakes are powerful tools that can be used for innovation or manipulation, depending on the intent behind their application. Below is a breakdown of legitimate uses that benefit society and malicious uses that pose serious risks.

Legitimate Uses of Deepfake Technology

1. Film and Television Production

Deepfake tools enable filmmakers to:

• De-age or age actors realistically without makeup or CGI (e.g., The Irishman by Martin Scorsese).

• Recreate deceased actors for posthumous scenes or tributes (e.g., Carrie Fisher in Star Wars).

• Replace actors in reshoots without re-filming entire scenes, saving millions in production costs.

Studios increasingly use AI-driven face-swapping and voice synthesis to streamline creative workflows while preserving artistic integrity.

2. Personalized Education and Training Avatars

Educational platforms and corporate training systems use AI avatars to:

• Deliver personalized lessons in multiple languages using one educator's likeness.

• Create interactive digital instructors that can speak with natural facial expressions.

• Simulate historical figures or role-play scenarios for immersive learning.

AI avatars like Synthesia or D-ID are now used in HR onboarding, compliance training, and remote education environments.

3. Accessibility and Assistive Technology

Deepfake voice cloning is used to help individuals with speech impairments or neurodegenerative diseases by:

• Creating personalized synthetic voices that closely match their original speech.

• Enabling communication through text-to-speech tools powered by voice avatars.

• Helping individuals with ALS or throat cancer retain their “own” voice digitally.

Companies like Lyrebird and ElevenLabs offer tools that preserve someone’s voice based on short recordings, revolutionizing digital accessibility.

Malicious Uses of Deepfake Technology

1. Political Disinformation and Propaganda

Bad actors use deepfakes to:

• Fabricate videos of politicians making controversial statements.

• Disrupt elections by manipulating public opinion with fake interviews or speeches.

• Fuel civil unrest or spread false narratives in conflict zones.

In 2023, a deepfake of Ukraine’s President Volodymyr Zelensky urging soldiers to surrender was briefly circulated on social media—highlighting the potential for weaponized misinformation.

2. Financial Fraud and Impersonation

Cybercriminals use deepfakes to:

• Clone voices of executives to trick employees into transferring large sums (CEO fraud).

• Forge video conference calls to approve fake deals or invoices.

• Target high-net-worth individuals through personalized phishing messages.

Arup Group lost $25 million in 2024 after criminals impersonated executives via a deepfake video call.

3. Social Engineering and Identity Theft

Attackers can:

• Use fake profile pictures or voice messages to build trust on social platforms.

• Impersonate recruiters, journalists, or colleagues in real-time calls.

• Exploit personal relationships for access to data, credentials, or sensitive assets.

Fraudulent LinkedIn profiles with AI-generated faces have been used to infiltrate organizations and gather intelligence.

4. Phishing Scams Using AI-Generated Voice or Video

Known as deepfake phishing, this involves:

• Leaving voicemail instructions from a cloned voice to change payment details.

• Recording fake videos of clients or executives to instruct urgent transfers.

• Sending fake audio replies in WhatsApp, Slack, or Teams to bypass suspicion.

In a 2025 test by a journalist, AI-generated voice clips successfully bypassed voice authentication in 3 out of 5 major banks.

What Is Deepfake Phishing?

Deepfake phishing is an advanced form of social engineering in which cybercriminals use AI-generated voices, images, or videos to impersonate trusted individuals—such as executives, colleagues, clients, or family members—in order to deceive victims and manipulate them into taking harmful actions.

Unlike traditional phishing, which typically relies on fake emails or lookalike websites, deepfake phishing adds a hyper-realistic audio-visual layer to the deception, making the scam more convincing and urgent.

How It Works:

• An attacker collects publicly available data: LinkedIn profiles, videos, podcasts, or webinars featuring a target (e.g., a CEO or CFO).

• Using voice cloning software (like ElevenLabs, Resemble.ai) or face-swapping tools, they generate a synthetic video or audio message.

• The victim receives a fake phone call, voicemail, or video call that appears legitimate, instructing them to:

  • Transfer money

  • Disclose sensitive credentials

  • Grant access to internal systems

  • Bypass normal procedures due to "urgency"

Why It’s Dangerous:

• Most humans cannot distinguish a deepfake voice or face in real time.

• These attacks often bypass traditional phishing filters (e.g., email scanning).

• They can be executed with low cost and minimal technical skill using free or low-cost online tools.

Real-World Cases of Deepfake Exploits

Case 1: Arup Group – Executive Impersonation via Deepfake Call

What Happened: In 2024, Arup Group, a multinational engineering consultancy, was targeted by attackers who used a deepfake video call to impersonate high-level executives. The employee on the receiving end believed they were speaking to a real C-suite leader and was instructed to wire $25 million to an external account.

Key Tactics Used:

• Realistic face and voice simulation via AI

• Use of insider knowledge to create context (e.g., referencing a project or urgency)

• Exploitation of remote work norms (video-only verification)

Impact: The company suffered a significant financial loss and reputational damage.

Response Measures:

• Rolled out biometric liveness detection for all executive approval steps

• Mandated face authentication + verbal passcodes in high-risk scenarios

• Reviewed internal financial approval workflows and tightened thresholds for large transfers

Case 2: South Korean Celebrity Deepfake Misuse – K-pop Star Karina

What Happened: In 2025, AI-generated promotional content featuring Karina, a member of the K-pop group aespa, was released without her knowledge or consent. Her synthetic face and voice were used to promote a virtual influencer and fashion campaign.

Key Issues:

• Ethical and legal concerns over non-consensual likeness usage

• Public outrage and loss of trust in media platforms

• Highlighted risks of AI-generated celebrity marketing fraud

Impact: Although not a financial scam, the case triggered lawsuits and raised global concerns over the unregulated use of AI in content creation.

Response Measures:

• South Korea's entertainment and tech industry began:

  • Deploying real-time AI content verification

  • Advocating for watermarking and origin tracking

  • Lobbying for stronger deepfake laws (passed in late 2025)

Case 3: Financial Sector Penetration Test – Voice Authentication Failure

What Happened: In a 2025 investigation, a journalist created AI clones of their own voice using free online tools and attempted to access their accounts at five major banks via phone-based voice authentication systems.

Results:

• Three out of five banks failed to detect the synthetic voice and allowed the authentication to proceed.

• One bank stopped the process due to subtle mismatches in phrasing.

• One flagged the activity after cross-checking additional security questions.

Impact: This test proved that voiceprint authentication alone is no longer secure in a world with accessible deepfake voice generators.

Response Measures:

• Affected banks began:

  • Phasing out voice-only authentication

  • Integrating multifactor authentication (MFA) systems

  • Testing real-time liveness prompts (e.g., reading randomized phrases)

Deepfake Detection Methods: How Modern Systems Spot AI Fakes

As deepfakes become more realistic and accessible, detecting them requires advanced and layered approaches. Below are the most widely used and emerging deepfake detection techniques, explained in detail:

1. Vision-Language Models (VLMs)

What it is: These models analyze both visual content (images or video frames) and associated language (transcripts, audio, or captions) to spot mismatches and unnatural patterns.

How it works:

• VLMs compare lip movement, facial expressions, and emotional tone with spoken or written text.

• They use common-sense reasoning to detect illogical behavior (e.g., a smiling face while saying something tragic).

Applications:

• News verification (to detect fake interviews)

• Political content screening

• AI-enhanced social media monitoring

Limitations:

• Less effective on silent videos or content without speech

• May struggle with real-time detection

2. Transformer-Based Visual Detection

What it is: Transformer models like Swin Transformers are designed to evaluate images or videos at a high level of detail, identifying inconsistencies in structure, texture, and context.

How it works:

• Unlike older convolutional models (CNNs), transformers process global image features more effectively.

• These models can detect subtle artifacts such as lighting anomalies, pixel-level inconsistencies, or facial warping.

Applications:

• Face-swapped videos

• AI-generated portraits

• Deepfake profile pictures

Limitations:

• Requires large, labeled datasets to train

• Can be resource-intensive

3. Patch-Level Forensics

What it is: Instead of analyzing the whole image or video at once, this method breaks content into small patches to examine each region for manipulation.

How it works:

• Algorithms scan each patch (e.g., an eye, corner of a mouth) for unusual patterns, compression noise, or inconsistent artifacts.

• If one patch is tampered while others are not, the system flags it.

Applications:

• Fake ID documents

• Image tampering in media

• Deepfake face masking

Limitations:

• Can miss high-quality forgeries where every patch is edited uniformly

4. Biometric Liveness Detection

What it is: A security layer used in identity verification systems to ensure the person interacting with a camera is physically present and alive, not a static video or deepfake.

How it works:

• Tests for natural responses: blinking, micro-movements, head turns, or reaction to lighting changes.

• Some systems ask users to perform random actions (e.g., smile, move left) during video authentication.

Applications:

• KYC processes in fintech and banking

• Secure video interviews

• Login protection for high-risk accounts

Limitations:

• Some advanced 3D deepfakes can bypass basic checks

• Requires high-quality camera input and stable internet connection

5. Audio Forensics and Voiceprint Analysis

What it is: Used to detect cloned or synthetic voices in phone calls, voicemails, or audio messages.

How it works:

• Analyzes speech patterns, cadence, pauses, pitch, and background noise.

• Flags inconsistencies like:

  • Monotone delivery

  • Lack of breathing sounds

  • Absent emotional inflection

Applications:

• Fraud detection in call centers

• Voice-based authentication protection

• Verifying public voice recordings

Limitations:

• May fail with high-quality AI voice models

• Not reliable in noisy environments

6. Metadata and File Forensics

What it is: Examines the digital fingerprint and metadata of media files, looking for inconsistencies that suggest tampering.

How it works:

• Checks EXIF data (timestamps, device info)

• Analyzes compression signatures

• Detects edit history (in professional editing software)

Applications:

• Journalistic investigations

• Legal evidence verification

• Court-admissible file analysis

Limitations:

• Metadata can be removed or spoofed

• Ineffective on media scraped from social networks

7. Blockchain and Provenance Technology

What it is: A newer approach where original content is cryptographically signed at creation, allowing viewers to verify its source.

How it works:

• Content creators embed digital watermarks or blockchain hashes into their videos/photos.

• Viewers or platforms can validate these signatures using secure APIs.

Applications:

• News agencies verifying footage

• Protecting brand identity

• Combating misinformation online

Limitations:

• Requires industry-wide adoption to be fully effective

• Doesn't detect deepfakes—only confirms authenticity of original

Deepfake Detection Methods

Recommendations for Small Businesses & Startups

1. Use Multi-Factor Authentication (MFA)

Why it matters: Many deepfake attacks succeed because businesses rely solely on voice or video verification for high-value transactions or sensitive approvals. A cloned voice over the phone can now mimic your CEO with frightening precision.

What to do:

• Always require at least two forms of identity verification: a password + token, or voice + SMS confirmation.

• For executive approvals, add a third layer — like biometric authentication or manual sign-off by a second person.

2. Train Staff to Spot Deepfake Cues

Why it matters: Your frontline employees are the first line of defense against social engineering. If they don’t know what a deepfake sounds or looks like, they won’t question an impersonated email, video message, or phone call.

What to do:

• Include deepfake awareness training in your regular cybersecurity program.

• Use real-world examples (like the Arup CFO scam) to show what to look for:

• Lip movements not synced to voice

• Slightly robotic tone

• Backgrounds that appear static or poorly rendered

• An unusual urgency (“I need this payment done now!”)

  
  

3. Protect Executive Media

Why it matters: Deepfake tools only need seconds of video or audio to clone a person’s voice or face. If your CEO has hours of speeches online, attackers have everything they need.

What to do:

• Scrub unnecessary videos of executives from YouTube, LinkedIn, or public folders.

• When publishing necessary media:

  • Add invisible watermarks

  • Avoid recording high-quality head-on videos

  • Reduce exposure by publishing in lower resolution

Bonus Tip: Add audio “trap words” into speeches (e.g., non-words or inside jokes). They’ll be missing or mispronounced in clones.

4. Adopt Liveness Detection Tools

Why it matters: Deepfake videos or images can fool static face recognition systems. That’s why attackers increasingly use fake video calls or forged IDs to gain access to bank accounts, crypto wallets, or HR portals.

What to do:

• Switch from traditional facial recognition to liveness detection tools that verify real human behavior — blinking, moving, or responding to prompts.

• Use video-based onboarding or ID verification for new clients or employees, with built-in AI deepfake detection.

5. Build a Deepfake Incident Playbook

Why it matters: In the event of a suspected deepfake attack, response time is everything. Delays can lead to irreversible financial loss or reputational damage.

What to include:

• Verification protocol: Who gets called when someone receives a suspicious video/voice message?

• Transaction freeze: Clear instructions to pause any payment or login attempt that seems out of character.

• Legal & PR contact: Assign one person for incident reporting to law enforcement and one for communication with customers if needed.

• Platform takedown policy: Have pre-filled abuse request forms for platforms like YouTube, Instagram, and TikTok in case of non-consensual deepfake posting.

Helpful Templates:

• SANS Institute Incident Response Plans

• NCSC (UK) business continuity planning kits

• NIST 800-61 guide to handling computer security incidents

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By 2025, deepfakes have become more potent—and more insidious. Businesses, media outlets, and individuals must employ multilayered detection strategies, stay abreast of technological advances, and operate within new legal standards to counter this expanding digital threat landscape.