Deepfakes Are on the Rise: How Worried Should We Be?

In recent years, the rise of deepfake technology has added a dangerous new dimension to the cybersecurity landscape. Deepfakes are synthetic media such as videos, images, or audio recordings that use artificial intelligence (AI) and deep learning techniques to realistically replace one person’s likeness or voice with another’s. The word deepfake combines “deep learning” (a type of AI that trains neural networks on large datasets) and “fake.” Deepfakes make it possible to create highly realistic but completely fabricated content, such as a video showing someone saying or doing something they never did, or an audio clip mimicking a person’s voice.

How Deepfakes Began

The origins of deepfake technology trace back to advances in computer vision and machine learning in the early 2010s. Researchers began experimenting with autoencoders and generative models, which are algorithms capable of reconstructing images by learning from large datasets. The real breakthrough came with the development of Generative Adversarial Networks (GANs) in 2014, introduced by computer scientist Ian Goodfellow and his colleagues. GANs consist of two neural networks, a generator and a discriminator that work against each other:

• The generator creates synthetic images or videos.
• The discriminator evaluates whether the content looks real or fake.

Through this back-and-forth process, the generator improves until the fake content becomes nearly indistinguishable from genuine media. Although GANs were initially designed for creative and research purposes, they soon gained attention for their potential misuse. By 2017, the first “deepfake” videos began circulating online, often featuring celebrity faces superimposed onto other bodies. These early examples quickly went viral and raised ethical, legal, and privacy concerns.

Technological Progress and Refinement

Since 2017, deepfake technology has evolved rapidly due to greater computing power, open-source tools, and the accessibility of training datasets. Some major stages of advancement include:

Improved Algorithms

Modern deepfakes now use advanced architectures such as StyleGAN, Diffusion Models, and Transformer-based frameworks to generate hyper-realistic images and videos. These models can capture subtle facial nuances like blinking patterns, muscle movements, and lighting consistency.

Real-Time Generation

Originally, creating a deepfake required hours or even days of GPU-intensive processing. Today, tools like DeepFaceLive and Avatarify enable real-time deepfake video streaming, allowing users to impersonate someone instantly during live calls or broadcasts.

Voice Cloning and Synthetic Audio

Beyond visuals, AI-driven voice synthesis has become a powerful component of deepfake technology. With only a few seconds of recorded speech, AI models such as Tacotron 2, WaveNet, and VALL-E can recreate a person’s unique voice, tone, and speaking style. These audio deepfakes are now being used in phone scams, fraudulent business calls, and impersonation attacks.

Text-to-Video Generation

Recent developments in multimodal AI have merged text generation with video creation. Platforms like Runway, Pika Labs, and OpenAI’s Sora can generate short video clips directly from written prompts. While these are designed for creative or commercial purposes, they also demonstrate how easy it is to synthesize convincing fake media without needing real footage.

How Attackers May Exploit Deepfakes

1. Inciting Violence

A malicious actor could create a deepfake video designed to provoke unrest or aggression. Using personal data, the attacker trains an AI or machine learning model to replicate a real individual’s face and voice and then adds inflammatory statements. The resulting video could then be anonymously uploaded to social media, with fake accounts amplifying it to spark outrage or division.

2. Corporate Sabotage

Attackers may use deepfakes to damage a company’s reputation by spreading false information about its products, executives, or brand. These fabrications can distort public perception, disrupt mergers and acquisitions, devalue stock prices, or unfairly sway competition.

3. Advanced Social Engineering Attacks

Deepfakes can make phishing and impersonation attacks more convincing. By mimicking trusted individuals, like CEOs, business partners, or clients, attackers can trick employees into transferring money, sharing credentials, or disclosing confidential information. In early 2024, an employee of a multinational company based in Hong Kong was tricked into transferring approximately US$25.73 million after participating in a video-conference call that was entirely fabricated using deep-fake technology. The employee received a message from the company’s UK-based Chief Financial Officer, requesting a confidential transaction. The employee joined a “video meeting” in which other participants appeared to be senior executives of the company. All of them were deep-fakes, and the fraudsters had taken publicly available video and voice samples of real people, altered them using artificial-intelligence face-/voice-swapping techniques, and played a pre-recorded session. Convinced by the apparent authenticity of the call, the employee followed instructions and made 15 transfers to five different bank accounts.

4. Corporate Liability Risks

As deepfakes become more realistic and widespread, businesses face growing legal and financial risks. Consumers deceived by deepfake scams may seek compensation for losses, while fraudsters could create fabricated evidence to sue companies. For instance, an attacker might produce a fake video showing an injury caused by a supposedly defective product, intending to extort money or damage the company’s credibility.

5. Cyberbullying

A deepfake can be used to humiliate or discredit a victim by placing them in fabricated compromising situations. For example, a deepfake video showing someone engaging in illegal acts could be distributed to their peers, employers, or teachers. Because social media rapidly spreads rumors, combining false claims with realistic fake media can destroy reputations and cause severe emotional distress, which could lead victims to self-harm.

6. Deepfake Pornography

In cases of revenge or coercion, a person might create explicit deepfake content using an ex-partner’s likeness. For example, an angry ex-boyfriend could threaten to release fabricated nude videos of his girlfriend to manipulate or blackmail her into staying in the relationship.

7. Election Manipulation

Deepfakes can play a powerful role in spreading political disinformation. Prior to an election, supporters of one candidate could release deepfake videos of an opponent making offensive statements or engaging in unethical behavior. Additionally, text-based deepfakes, such as AI-generated posts or comments, can be used to influence online narratives, polarize voters, erode public trust, and destabilize the democratic process.

8. Synthetic Identities in Digital Onboarding

Financial institutions and service providers increasingly use video KYC (Know Your Customer) processes. Deepfakes can generate entirely synthetic identities that pass these verification steps, giving criminals access to bank accounts, credit, or even government services.

Defending Against Deepfakes

As deepfake and synthetic media technologies continue to advance, the challenge of distinguishing real from fake becomes increasingly complex. However, several emerging strategies and proactive defenses can help reduce the risks posed by these manipulative digital creations.

1. Multi-Factor Authentication (MFA)

Relying solely on facial recognition or a single biometric identifier is no longer sufficient in an era where synthetic media can replicate human features and voices with alarming precision. MFA introduces additional layers of verification, such as device-based confirmation, time-limited passcodes, or behavioral biometrics like typing rhythm and mouse movement patterns.
By combining these multiple checkpoints, organizations can reduce the chances of unauthorized access, even if a deepfake successfully mimics a legitimate user’s face or voice. In high-security environments, adaptive MFA systems can also assess contextual factors like login location or device fingerprinting to flag anomalies in real time.

2. Deepfake Detection Tools

Artificial intelligence can also be used to fight AI-generated threats. Modern detection tools use deep learning models to identify microscopic details in audio and video that often escape human notice. These include unnatural blinking patterns, inconsistent lighting reflections, mismatched lip synchronization, and irregular sound frequencies.
Despite these advances, detection remains a constant “arms race.” As generative AI systems become more sophisticated, forgers learn to eliminate telltale signs, making it essential for organizations to continuously update and retrain detection algorithms. Collaboration between tech companies, academia, and law enforcement is necessary to stay ahead of evolving deception tactics.

3. Digital Watermarking and Provenance Tracking

To ensure the authenticity of digital content, researchers and technology firms are increasingly turning to watermarking and provenance-tracking solutions. Cryptographic watermarks, which are embedded invisibly within videos or images, can help verify whether content was created or altered using authorized tools.
In parallel, blockchain-based provenance systems offer tamper-proof records that track the lifecycle of digital media from creation to publication. Initiatives like the Coalition for Content Provenance and Authenticity (C2PA) are working toward standardized frameworks that allow users, journalists, and platforms to verify the legitimacy of visual content before sharing it.

4. Employee Training and Awareness

Technology alone cannot counter synthetic threats without human vigilance. Employees, especially those in finance, communications, and executive roles, should be regularly trained to recognize the risks posed by deepfakes and AI-driven impersonation.
Organizations should implement clear verification policies that require cross-channel confirmation for sensitive actions, such as transferring funds or changing account credentials. For example, if a video call or voice message appears to come from an executive requesting urgent action, staff should be required to confirm the request through a secondary channel, such as a secure internal messaging system or direct phone call.
Regular awareness campaigns, simulated phishing tests, and scenario-based training can reinforce a culture of skepticism and verification, reducing the likelihood of successful social engineering attacks.

Conclusion

As generative AI becomes more accessible, the barrier to creating convincing deepfakes continues to fall. This democratization means that what once required specialized skills can now be done with off-the-shelf AI tools and minimal data. Deepfakes challenge our most basic assumptions about trust and identity in the digital age. In response, cybersecurity frameworks must evolve from “verify credentials” to “verify reality.”