Deepfakes have risen in prominence in recent years due to revolutionary advancements in artificial intelligence, particularly in machine learning and deep neural networks. These technologies enable the creation of highly realistic and often deceptive video, image, audio, and text. 

The rapid evolution of deepfake technology has prompted widespread concern among cybersecurity experts as AI-fueled attacks on businesses continue to surge. Deepfake attacks were the second most frequent cybersecurity incident experienced by businesses in the last 12 months. Already, over 30% of businesses surveyed globally report cybersecurity breaches involving generative AI. To meet this challenge, enterprises can leverage deepfake detection software to protect their business, workers, and customers from fraud, theft, and serious reputational damage.

Deepfake Detection Software: A Crucial New Security Measure

Increasingly, companies find themselves unprepared for the new methods of fraud enabled by AI. In February, a finance worker employed by a multinational firm transferred $25 million into fraudsters’ accounts after the firm’s CFO requested the transaction in a group video conference call. The call was a deepfake, and the CFO’s likeness was the product of generative AI created by cybercriminals. We are seeing similar cases of attempted deepfake impersonations of executives, IT workers, and customers, created with the goal of breaching the company’s security workflow, stealing assets and data, and bypassing KYC and AML measures. 

But deepfake attacks are not always so direct. Malicious actors can leverage deepfakes in disinformation campaigns aimed at smearing the company’s reputation. A single video portraying the likeness of the CEO making offensive statements that were never really spoken can circulate on social media much faster than they can be debunked. Similarly, fictional media showing faulty products or bad factory conditions can damage a company’s reputation permanently, and lead to loss of customers, shareholder value, and disruptions in business operations. 

The Role of Deepfake Detection Software

Deepfake detection software uses AI-fueled deep learning models and neural networks to identify manipulated media by analyzing inconsistencies in visual, audio, and metadata features. These models examine artifacts such as unnatural facial movements, lighting inconsistencies, and voice artifacts. Detection models continue to achieve greater accuracy in distinguishing authentic content from deepfake thanks to continuous updates with datasets that reflect the latest capabilities of generative AI.

Deepfake detection software identifies and flags potential deepfakes in real-time and at scale by continuously analyzing incoming media for anomalies. Reality Defender employs multiple AI models to scan each media type with several approaches in mind, and produces reports that assign each type of analyzed media a score ranging between 1 and 99, providing a precise metric for how confident we are that the media in question is a deepfake. Along with this score, our deepfake detection software highlights the inconsistencies that betray the media as synthesized, allowing companies to take action on a case-to-case basis and publicize the results to inform the public and shareholders. 

In the age of AI-fueled cybercrime, a multi-pronged approach to defense is a must, and companies will benefit greatly from utilizing deepfake detection software alongside other measures, such as biometric verification and provenance watermarking.

Real-World Examples of Successful Detection

Reality Defender serves a diverse roster of clients, including businesses, financial institutions, digital platforms, media companies, and governments, and maintains high rates of successful deepfake detection across industries and use cases. Recently, we partnered with a major bank that provides financial services to hundreds of millions of customers in tens of billions of transactions per day. To address the onslaught of deepfake voice calls inundating the bank’s call centers, we have successfully integrated our detection models into the institution’s workflow, protecting its workers and customers from fraudulent transactions.

We work with tier-one social media platforms to help root out deepfakes and AI-enabled bots, support major media companies in helping reporters, editors, and fact-checkers protect their work and reputation from the threat of AI-enabled disinformation efforts, and assist governments in stopping disinformation efforts before they can manipulate the outcomes of democratic elections. Although these use cases stretch across a variety of fields and industries that have little in common, all of them share a common threat—malicious deepfakes—and a common solution in widely integrable deepfake detection software.

The need for flexibility across workflows is why we designed our detection tools to be platform-agnostic, providing a convenient web application and turnkey API that can be incorporated into any pre-existing security systems. Reality Defender provides a multi-modal approach that integrates cutting-edge research via regular updates, targeting current and future trends in deepfake manipulation. Leveraging the power of AI to stop the malicious misuse of AI, we are committed to providing all-encompassing deepfake detection software solutions to protect businesses from fraud, theft, and reputational damage. 

Deepfake technology will only continue to improve and pose a greater threat. It is crucial for businesses to remain alert and proactive by permanently incorporating deepfake detection into their security strategies.