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What do the POTUS, the G.O.A.T cricketer, and a Hollywood icon have in common? Well, while it may be hard to find a connecting thread between them, they all have fallen prey to the rapidly growing technology of deepfakes.
Barack Obama, Sachin Tendulkar, and Tom Cruise have all been victims of deepfakes – a term that has become synonymous with the world of fake videos. Deepfake videos have seen an alarming rise in the past few years.
As per a report, deepfake videos have risen more than 550% since 2019. And with advancements in technology, they are becoming increasingly difficult to detect. Additionally, the use of digital watermarks is emerging as a method to verify the authenticity of media, offering a potential solution to the deepfake challenge.
So, how do you identify a deepfake? What exactly is it, and why are people so worried about them? This comprehensive guide will answer all your questions and more.
What is a deepfake: Understanding deepfake videos and images
Deepfake, a portmanteau of ‘deep learning’ and ‘fake’, refers to the AI-based technology used to produce or alter video content so that it presents something that didn’t actually occur. Essentially, it’s a sophisticated form of digital impersonation.
A striking example is the deepfake of Mark Zuckerberg claiming control over billions of people’s stolen data, a video that never actually happened but, in this era of fake news, appeared alarmingly real.
The creation of deepfakes involves training machine learning models on a substantial number of images of a target person, enabling the artificial intelligence algorithm to understand and mimic the person’s facial expressions and movements. This process, known as the deepfake generation process, leverages convolutional neural networks and other AI techniques to create realistic videos by identifying and replicating spatio-temporal inconsistencies and facial artifacts.
The technology’s uncanny accuracy can be witnessed in the deepfake videos of Barack Obama created by Jordan Peele, where the former president appeared to utter words he never actually said.
How deepfakes work
A deepfake video works by using a type of machine learning called deep learning. This involves training a model, typically a Generative Adversarial Network (GAN), on a vast dataset of images or videos.
Generative Adversarial Networks are comprised of two parts: the generator, which creates images, and the discriminator, which tries to differentiate between real and generated images. The two systems work against each other, hence the term ‘adversarial’. As the generator improves, it becomes better at fooling the discriminator.
Over time, the system can generate impressively realistic deepfakes, mimicking the target’s facial expressions, voice, and even mannerisms. This intricately complex process is what makes deepfakes both innovative and concerning.
This is particularly important when you consider the implications of fake images used during onboarding and user verification. Here’s an image that’s been doing rounds of the internet — it’s actually a deepfake video of a person completing the verification process!
Read more: How do deepfakes work?
Similarly, audio deepfakes use comparable machine learning techniques to mimic voices, making them a significant concern for fraud and scams due to their ability to convincingly replicate a person’s speech.
Evolution of deepfake technology
Deepfake technology has advanced dramatically since its inception. Its roots can be traced back to academic research in the 1990s, but it was the introduction of machine learning and AI that propelled the technology to new heights. Early deepfakes were rudimentary, requiring significant technical expertise and resources. With the advent of artificial intelligence, deepfakes became easier to create and more convincing.
Platforms like DeepFaceLab and apps like ZAO made deepfake creation accessible to non-experts, leading to a surge in deepfake content. With continued progress in artificial intelligence and machine learning, creating deepfakes is becoming easier than ever. The ability to create personalized videos using deepfake technology is a testament to its growing accessibility and application, from corporate training materials to entertainment. It is also becoming increasingly difficult to distinguish deepfakes from genuine content. Deepfakes are also becoming massively popular on social media platforms, which can further propel their creation and spread.
The dark side of deepfakes: dangers and misuse
While the technological prowess of deepfakes is undeniable, their potential for misuse is alarming. As they become more accessible and harder to detect, deepfakes pose a significant risk in areas such as misinformation, identity theft, and privacy invasion. Let’s delve into some of these dangerous implications.
Another disturbing trend is the rise of deepfake porn and deepfake pornography, where the technology is used to create or alter pornographic material without the consent of the individuals involved, often targeting female celebrities. This unethical use of deepfakes has led to significant ethical concerns and legal actions. Various platforms have started to ban and remove non-consensual deepfake content, and countries and states are implementing laws specifically against deepfake pornography to protect individuals from this invasive form of digital abuse.
Political deepfakes
One of the most significant concerns surrounding deepfakes is their potential to influence political outcomes. With the ability to manipulate and distort information, deepfakes can sway public opinion and disrupt democratic processes.
In 2018, a deepfake video featuring former US President Barack Obama went viral, showing him delivering a message that he never actually said. While this particular instance was meant as a demonstration of the technology, it raised questions about how deepfakes could be used to deceive and manipulate people for political gain.
Deepfake fraud
In addition to political implications, deepfakes also pose a significant risk in the realm of fraud. With the ability to create convincing videos and audio recordings, scammers can use deepfakes to impersonate individuals and deceive people into giving away sensitive information or money.
For instance, in 2019, a CEO was tricked into wiring $243,000 to a scammer who used a deepfake audio recording of his employer’s voice to make the request seem legitimate. This type of fraud is expected to become more prevalent as deepfakes continue to improve in quality and accessibility.
The cost of deepfake fraud
As we explore the darker implications of deepfakes, it’s crucial to understand that these fabricated realities pose not just personal, but also substantial financial risks. Let’s delve into the economic implications of deepfake fraud to grasp its full impact.
Reputation loss due to deepfakes
Deepfakes have a profound ability to tarnish reputations and destroy personal and professional lives. By creating false scenarios, these synthetic media can cause severe damage to an individual’s public image.
For example, a fake video of a highly respected individual acting inappropriately, making controversial statements, or even pornographic videos can lead to a loss of credibility, negatively impacting the reputation not just of the individual, but also of the organization they represent. This might result in decreased investor confidence and a drop in stock prices.
Monetary loss due to deepfake fraud
As mentioned previously, deepfakes can also lead to substantial financial losses. These synthetic media are becoming a new tool for fraud, with scammers using them to impersonate CEOs or other high-ranking officials to deceive employees into transferring funds or revealing sensitive information. As we saw in the previous example of CEO impersonation, deepfakes can have a significant impact on an organization’s finances.
Read more: How To Prevent Deepfake Scams In User Onboarding
How to detect and combat deepfakes: Deepfake detection strategies
The rapidly advancing technology behind deepfakes makes detection a constant race. In response, the deepfake detection challenge has been initiated by leading technology companies and research teams. This global effort aims to accelerate the development of methods to identify manipulated content, enhancing the effectiveness of deepfake detection technologies. However, several strategies and tools can help individuals and organizations identify and combat this fraudulent content. Let’s examine these techniques in detail.
Simple methods
- Unnatural Movements: The presence of unnatural body or movements in a person’s face can indicate a deepfake. Real human movements are fluid and consistent, but deepfakes may display jerky or abrupt movements. This is because most deepfake algorithms focus on facial features, often neglecting to convincingly replicate the rest of the body.
- Asynchronous Audio and Video: The synchronization between audio and video is another indication of a potential deepfake. Genuine videos maintain a consistent sync between the visual and audio elements. However, deepfakes often struggle to synchronize these aspects perfectly, leading to noticeable mismatch or delay between the video and corresponding audio.
- Inconsistencies in Colors and Shadows: Color and shadow inconsistencies can reveal a deepfake video. Real videos maintain consistent lighting, color grading, and shadows throughout. In contrast, deepfakes often display inconsistencies in these areas, as artificial intelligence still struggles to replicate real-life lighting and shadow conditions accurately.
Read more: How to detect AI-generated selfies
Advanced deepfake detection tools
As the creation of deepfakes has become more sophisticated, so too have the techniques for detecting them. AI-powered deepfake detection tools have emerged as particularly effective. These utilize highly accurate, well-trained models with diverse datasets to pinpoint the subtle anomalies that can indicate a deepfake.
These AI models study patterns in thousands of videos, learning to discern the authentic from the manipulated. They can detect details that may slip the human eye, like slight distortions in the background or minute facial changes, providing a highly accurate detection rate.
Another advanced method is using facial recognition systems with liveness checks. This technique can prove particularly effective in real-time situations such as video calls or live streams. Liveness detection verifies the ‘presence’ of a real person. This is done by analyzing responses to specific challenges that a pre-recorded or manipulated video would fail. For example, blinking in response to a flash, moving the head in response to a prompt, or answering an unexpected question.
These methods raise the bar for deepfake detection, making it harder for fraudsters to deceive systems and individuals.
Know more: HyperVergedeepfake detection
We have a small game for you to play. Step into the shoes of an AI-powered deepfake detector and guess which of the photos is a deepfake and which one is real. Good luck! Try for yourself whether you can ‘get the fake out‘
Read more: how to spot a deepfake?
Are deepfakes illegal?
The legality of deepfakes is a complex issue, as it often depends on the context and intent behind their creation and use. On one hand, deepfakes can be used for legitimate purposes such as entertainment, satire, or artistic expression.
On the other hand, deepfakes can also be used for malicious purposes, such as identity fraud, political manipulation, defamation, or to spread false information. These deceptive uses of deepfakes can inflict significant harm.
Whether it’s ruining a person’s reputation, manipulating public opinion during an election, or tricking individuals into divulging confidential information. In such cases, the creation and use of deepfakes can be illegal under laws related to identity theft, defamation, or fraud.
The challenge lies in finding a balance that respects the creative and innovative uses of deepfakes, while simultaneously protecting individuals and society from their potential harm.
This requires clear regulations that define the legal boundaries of deepfake use, as well as effective enforcement mechanisms to ensure these rules are followed. And given the global nature of the internet, this is a task that requires international cooperation and dialogue.
The future of deepfake detection: Challenges and opportunities
Detecting deepfakes today presents a formidable challenge. The sophistication of AI-generated deepfakes has reached a point where it’s increasingly difficult to distinguish between synthesized and real content, even for trained human eyes.
Advances in AI and machine learning have enabled the creation of incredibly realistic deepfakes, blurring the lines of reality in digital content and raising major concerns about their potential misuse.
Despite the obstacles, the same technology that makes deepfakes possible – AI, is also our greatest asset in combating them. Machine learning models can be trained to spot the subtle inconsistencies in deepfake videos that human observers might miss.
By analyzing thousands of real and deepfake videos, these models can learn to identify the telltale signs that discern real images from deepfakes. AI technology is powering deepfake detection tools that are sophisticated and robust enough to counter the threat of deepfakes effectively.
Want to know more about how to handle deepfakes? Download our e-book, the “Non-Nerdy Guide to Deepfakes and How to Combat Them,” for practical insights on handling and countering deepfake challenges.
HyperVerge’s fight against deepfakes
At HyperVerge, we’ve developed a robust deepfake detection solution. Employing advanced machine learning algorithms, it scrutinizes every minute detail in videos to distinguish real content from synthesized ones. This solution aptly exemplifies the application of AI in not just creating life-like deepfakes but also in effectively countering them.
Want to experience the power of industry-best facial recognition and liveness check? Sign up to see our deepfake detection solution in action.
