A Definitive Guide to Deepfake Social Media: Evolution, Creation & Detection

It is highly threatening to the integrity of world security where AI deepfake images of world leaders, celebrities, and influential people are disseminated over the internet to spread fake speeches to fool people creating personal or political tensions. Researchers found only one porn deepfake video in 2016, while the number significantly soared in 2023 reaching 143, 733, stressing most of the deepfakes videos show pornographic content and how concerning the matter is. 

In what ways the proliferation of AI deepfakes have posed potential threats to digital media and online interactions? What are the possible threats posed by deepfakes images or videos and how is it damaging the public trust and confidence in the online community? What tactics do cybercriminals employ to generate hyper-realistic deepfakes and how do they exploit advanced AI technology? What significant measures can be adopted to address the challenges posed by the massive spike in AI deepfakes? How can advanced facial recognition technology contribute to detecting and curbing the deepfake menace? This article will answer all these questions and handy knowledge on deepfake impact on social media. 

The Advent of Deepfake Social Media: A New Form of Digital Manipulation 

Fake digital content abetted by technological innovation promulgated over the internet poses serious threats to social media platforms and real-world applications, blurring the boundary between real and fake. One of these digital manipulation technologies is AI deepfakes, using AI algorithms and machine learning to generate hyper-realistic fabricated images or videos that are even hard to detect by human eyes. 

Undeniably, deepfake technology offers astonishing applications in generating visual effects and media productions. However, the negative use cases of deepfakes predominantly subjugate the positive use cases, stressing the imperative to foster detection strategies to curb deepfake social media. 

Public figures are the most obvious targets of deepfakes, where artificially fabricated stories or circumstances are disseminated that never really occurred, deceiving the public and posing threats to the credibility of the online community. The widespread propagation of user-created deepfakes on online platforms like Facebook, YouTube, or Twitter is experiencing a significant rise. 

The Story Behind: How AI Deepfakes Are Taking Over Social Media?

The timeline for the evolution of deepfake creation is briefly expanded below 

• The early developments of deep fake technology can be traced back to 1997, when Christoph Bregler, Michelle Covell, and Malcolm Slaney published a paper and laid the groundwork to develop a new program ‘Video Rewrite Program’. The proposed program, could produce facial movements based on the audio input and didn’t just rely on video editing features, but potentially used neural networks to alter audio and video data. This innovative research work stimulated researchers to make big achievements in facial recognition technology and significant advancements in creating highly convincing deep fakes in the early 2000s. 

• Following the previous works, a new algorithm named ‘Active Appearance Models’ was developed and gained popularity in no time. The algorithms using statistical models improve facial feature analyzing capability, being able to accurately cross-match facial features against the input images. Active Appearance Models largely rely on Generative Adversarial Networks (GANs) to effectively analyze facial features from a static image or live video and reconstruct the facial images. Owing to rapid developments in this area, it was potentially possible to generate deepfakes by 2016, making use of consumer-grade hardware. 
• Manipulating visual data is picking up steam and in 2017 Reddit experienced a soar in deepfake creation and gained popularity in the Reddit community. A now-deleted subreddit named r/deepfakes having nearly 90,000 members community, was incuplated in sharing pornographic deepfakes of renowned celebrities. 

Talking about the creation of deepfakes during 2019 to 2020, an increase from 14, 679 to 49, 081 was recorded by Sensity, a threat intelligence company. At least 4,000 celebrities fell victim to deepfake pornography reportedly, posted on most visited deepfake websites, out of which nearly 250 were British actors. 

Inside Deepfake Creation: From Data Collection to Deception 

Advanced deep learning including autoencoders and GANs are widely used to generate hyper-realistic and convince deep fake images or videos. These algorithms analyze and interpret facial features, micro-expressions, or movements and construct facial images or videos extremely analogous to input images. To better understand let’s explore the step-by-step overview of deepfake creation 

Deepfakes videos are often generated by using a combination of encoder and decoder, often within the framework of GANs. The encoder receives the input facial images, analyzes the data, extracts the facial features and the data is delivered to the decoder. The decoder constructs manipulated faces and this process is continued until the targeted results are achieved.

• Cybercriminals invest much time and energy to collect facial images of the targeted individuals by using social engineering tactics, identity theft, phishing, or taking images from social media accounts. 
• GANs consist of two parts generators and discriminators and are trained on large-volume datasets to give sharp output. The deepfake images or videos are created using the generator, while the discriminator evaluates the produced results and distinguishes them from real ones. 
• The next step is facial mapping where the facial features of the person from the dataset aligned targeted individual in an image or video. 
• The output images or video undergo refinement to enhance the quality and authenticity of output, and this process goes on until the discriminator fails to detect the difference between real and fake. 
• Any inconsistencies or visual defects are refined to produce highly realistic deepfakes.  

Implementing Liveness Detection Against AI Deepfake Detection 

The advent of AI deepfakes presents significant challenges to the integrity of digital identities and destroys public trust in online content. To effectively detect digitally manipulated media, biometric authentication solutions must deploy liveness detection to confirm the authenticity of the claimed identities or credibility of digital content shared on social media platforms.

• Onsite liveness Detection 

The primary goal of onsite liveness detection is to ensure the biometric data captured from a live person and the person is genuinely available for authentication in real time, accurately distinguishing live persons from digitally manipulated identities.  Since the data is processed quickly and in real-time, it offers instant authentication and actively flags deepfakes or manipulated identities. 

• Offsite liveness Detection 

It refers to ensuring the liveness of biometric data from already captured images or video present on social media platforms or from any other resources, to confirm whether the person has a live or fabricated identity. This approach can handle a large volume of authentication requests thus making it possible for platforms having an enormous number of users and ensuring that platforms are protected against threats of deepfakes. 

• Single Image Liveness Detection 

This approach confirms the authenticity of the digital identity presented to facial recognition technology by capturing a single image. The captured image is analyzed and the expressions are interpreted to confirm that it’s coming from a live person and anomalies are detected in real time. 

• Multi-Frame Liveness Detection

Multi-frame liveness detection relies on multiple images or frames to evaluate the presence and liveness of the person by analyzing blinking, micro-expressions, or movements. Both single-image and multi-image liveness fall under the category of onsite liveness detection, as the biometric data is processed in real time and authentication is performed swiftly. 

Also Read: Celebrity Deepfake | A Rising Threat with Reputational and Financial Consequences

The Role of Facial Recognition Technology in AI Deepfake Detection

AI deep fakes are sophisticated to the extent that they often dodge biometric authentication systems and get access to services or platforms, exploiting the integrity of the digital world. Facial recognition in itself isn’t sufficient to detect and mitigate the rising threats. 

• Integrating advanced AI algorithms and machine learning models trained on real and fake data to make them capable of detecting anomalies and inconsistencies when some fabricated identity is seeking to gain authentication.  

• Deploying texture analysis in facial recognition technology can actively evaluate inconsistencies in skin texture, tone, wrinkles, and blemishes can facilitate the systems to distinguish between a real person and a spoofed identity. 
• Biometric liveness detection when deployed in facial recognition technology, allows for accurate verification of genuine individuals, flagging spoofed or manipulated identities in real-time. 

Defeat AI Deepfakes with Facia’s Liveness Detection 

To stay ahead of the curve in the fight against digital deception, it’s crucial to deploy facial recognition technology that not only focuses on accurate authentication but also concentrates on implementing advanced technologies like AI algorithms, or deep learning to detect spoofed identities. Stay ahead of AI deepfakes with Facia, iBeta level 2 compliant, and aligning with ISO 30107-3 Presentation Attack Detection. This advanced facial authentication technology is highly committed to providing clients with the most advanced and reliable ID verification by deploying biometric liveness detection and actively flagging spoofed attempts.   

Closing Thoughts 

The deepfake impact on social media can’t be overlooked, as thousands of celebrities have fallen victim to the trap of manipulated images and videos, spreading fake news, or tormenting reputational image. Decision makers are stressing to curb the alarming surge in AI deep fakes that mislead voters and can even sway election outcomes, resulting in political distress and social unrest. Many jurisdictions are issuing stringent guidelines for social media platforms to evaluate digital content before granting access for sharing, authenticate whether genuine individuals are posting the content and immediately block any attempts of AI deepfakes to make the digital world a safer zone.