What if someone shows you a video that you never recorded? We are sure that you will get shocked and angry for using your face for a fake purpose. This trend is becoming commonplace in today’s world.
Recently, Twitter has been bursting with a viral video of Morgan Freeman urging us to question reality. The only difference in that video is that he is not the real Morgan Freeman. But it is a digital replica of him, a big example of deepfake technology.
Originally posted by a YouTube channel named Diep Nape, the video is creating a big wave nowadays. The video resurfaced on Twitter and gained over 6.5 million views.
The use of deepfake technology is definitely growing significantly. But the best part about the deepfake technique is that these videos can be detected. If you want to know how you can detect deepfake videos, you must read this article.
It will provide every necessary detail about deepfake detection. So let us dive into the details of deepfake technologies.
Deepfake Detections: Overview
Deepfakes are fake videos created using machine learning, software, and face-swiping. These are computer-created videos that depict statements, events, or actions that do not happen in the real world.
Deepfakes are distinct from other types of false information due to their extremely difficult-to-detect nature.
Generally, deepfakes are created using techniques like ‘generative adversarial networks, or GAN. GAN can look at thousands of images and produce a new image that resembles those images but is not an exact replica.
GAN can also be used to create new audio from existing audio and new text from existing text. The technology used to create deepfakes can create faces based on landmark points, such as facial expressions, corners of eyes and mouth, etc.
What Are Deep Learning Approaches?
Deepfakes techniques are part of this technology. Deep learning technology adapts its several approaches for deepfakes creation. Below are the top approaches to deep learning-
- (GANS) Generative adversarial networks – It is a practical method of machine learning that Lan Good and his team created in 2014. This approach is master in creating a new data set with existing ones.
- Recurrent Neural Network (RNN)– Recurrent Neural Network or RNN is a type of artificial intelligence commonly utilized in speech recognition and natural language processing. It recognizes data sequential characteristic and use pattern for predicting the next possible scenario.
- Convolutional Neural Network (CNN) – The convolutional neural network or CNN is used to recognize images and other tasks related to pixel data.
- Long short-term memory (LSTM) – LSTM is a variety of RNN or recurrent neural networks with the capability of learning long-term dependency. It can process the entire data, apart from a single data point.
Pros And Cons Of Deepfake Detections
Deepfakes were used for entertainment purposes in their early stages. However, as technology has advanced, its use has expanded. Like other technologies, deepfakes also have some advantages and disadvantages.
Let us begin with the pros of deepfakes.
Pros
Deepfakes technology is relatively famous for its cons. But several pros can surprise you. Below are the top pros of deepfakes-
- Bring Back The Loved Ones
Deepfakes technology can help you bring back those loved ones who are no longer in this world. You might think, How would it be possible? But the reality is that it can help you bring back your loved ones through video.
Do you remember the maker of Fast and Furious bringing Paul Walker back in Fast and Furious 7? However, they used CGI and VFX methods at that time. But deepfakes can perform the same job more affordably and easily. Makers of Fast and Furious do not perform deepfakes because the movie was released in 2015, and deepfakes became widespread in 2017.
- Hyper-Personalisation
Deepfakes can also give rise to hyper-personalization in marketing. This technology can alter the skin tone of dull skin models, so the customer can experience how the product would look on them. This process can help a brand increase inclusivity and reach a wider audience with its campaign.
- Low-Cost Marketing Campaign
Deepfakes can also give rise to low-cost marketing campaigns because you do not need a real actor. The marketer only needs to buy a license for the actor’s identity. They can then use the previous video recording of an actor, insert a dialogue from the script, and create a new video. They may save a ton of time and money in this way.
Cons
You might have seen several fake videos of well-known personalities. These videos were only created with the help of deepfakes. Apart from that, deepfake has the following cons:
- Privacy Problem
The first con of deepfake is that it promotes a lack of privacy. Every human in today’s world uses social media, like Facebook, Instagram, and more. Many people share their pictures on their social media accounts.
A deep fake can take anyone’s photo from social media and use it in their fake content. This can destroy anyone’s image in the world. That is why the privacy problem is among the top cons of deepfakes that must be considered.
- Generating Fake News
Deepfake can also be used to spread fake news. Recently, a video of Andrew Garfield went viral, indicating that he was part of Spidermen No Way Home. But that was a piece of fake news. The video was created with deepfake technology.
- Spamming
Deepfake technology can also give rise to scamming online. Scammers can use this technology by taking a recording of an actual incident and converting it into a new dialogue to mislead people. Several con artists are using this technology to defraud people.
Is It Easy To Create And Detect Deepfakes?
Deepfakes can be easily created with the help of deep learning tools or artificial intelligence tools. However, detecting deepfakes is too tricky for a human.
Steps Involved In Creating A Deepfake?
Creating a deepfake requires proper dedication and tools. Apart from it, you must follow the following steps to create a deep fake:
- Gathering video – Gathering video requires a few minutes of 4k videos and destination footage. All videos you gather must have the same facial expression, eye movement, and head movement. In short, every aspect of the video must be similar. If not, various visual artifacts will be displayed during the swapping process.
- Extraction process – Each video will be broken down into frames. The face will be identified within each frame, and approximately 30 facial landmarks will be identified to demonstrate as an anchor point for the model to learn the exact location of facial features.
- Training process – Each set of aligned faces will be input into the training network. A general schematic of the decoder and encounter for training will be shown in pictures. After extraction, batches of aligned and masked faces will be fed into the same encounter network.
The latent space object will be passed separately through the decoder network for each photo to recreate each picture set separately. The replicated face will be compared to the original picture, and the encoder and decoder networks will adjust their weights using a loss function and backpropagation.
This continues for a subsequent group of faces until the required number of epochs is reached. The users decide when to terminate the training by visually inspecting faces for quality.
- Conversion – Deepfake is created into a conversion step. The assigned and masked input face A will feed into the encounter. Remember, this encounter has learned representation of face A (original one) and face B (replica). When the encounter output is passed to the decoder, it will be attempted to generate face B. The conversion step is a one-way process from encounter to decoder. The result of the conversion step must be put together by other software to form a video.
- Post process – The post process is the primary and time-consuming process of deepfake. Minor errors are easily removable in this process. However, you cannot replace significant errors in this process. This is where deepfake software frameworks process the final editing of the video.
How Can We Identify Deepfake?
Deepfake detection is impossible for an average human. However, you can detect them by considering the following things:
- Who and why is sharing the video?
- What is the source of the video?
- Time and location where video shooted
- Is the person in that video saying something you never expected from them?
- Is video promoting any agenda?
Poor-quality deepfakes are easier to spot. The lipsync might be impaired, or the skin tone might be patchy. Poorly rendered teeth and jewelry can also be the spot for detecting deepfakes.
Do Deepfake Videos Help Spread Wrong Information On Social Media?
Yes, deepfake videos can help spread wrong information on social media. There are several examples, like the deepfake video of Obama, that prove that deepfakes can spread fake news on social media.
Who Can Use It And How?
Anyone can create deepfake videos to manipulate videos, images, and audio into never-happening incidents. You can also create deepfake videos with the help of software like MyHeritage, D-ID, and more.
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Conclusion: Deepfake Detections
Deepfakes videos have become a common part of today’s world. Almost every month, anyone’s fake video goes viral on social media. Indeed, the misuse of this technology is extremely high. But this technology has uncountable pros, some of which are mentioned above. Detecting deepfake is not an easy task, but you can do it with careful observation of a fake video.
FAQs
The Deepfakes program is built with GANs and two algorithms. One forge Deepfakes and another identifies flaws in forgeries that can be corrected. This is because deep fakes look realistic and are hard to detect.
The data set used by deepfake is forensic and VID-TIMID. Deepfake uses the CNN algorithm.
Yes, deepfake uses neural networks, which are the basis of deepfake technology.
