How to Spot a DeepFake in 2021

Wondering about the best ways to spot a deepfake? In this video, learn about a breakthrough US Army technology that uses artificial intelligence to find deepfakes.

►Read the full article: https://www.louisbouchard.ai/spot-deepfakes

►Test your deepfake detection capacity: https://detectfakes.media.mit.edu/

►DeepFakeHop: Chen, Hong-Shuo et al., (2021), “DefakeHop: A Light-Weight High-Performance Deepfake Detector.” ArXiv abs/2103.06929

►Saab Transforms: Kuo, C.-C. Jay et al., (2019), “Interpretable Convolutional Neural Networks via Feedforward Design.” J. Vis. Commun. Image Represent.►OpenFace 2.0: T. Baltrusaitis, A. Zadeh, Y. C. Lim and L. Morency, "OpenFace 2.0: Facial Behavior Analysis Toolkit," 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 2018, pp. 59-66, doi: 10.1109/FG.2018.00019.

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While they seem like they've always been there, the very first realistic deepfake didn't appear

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until 2017.

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It went from these first-ever resembling fake images automatically generated to today's

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identical copy of someone on videos, with sound.

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The reality is that we cannot see the difference between a real video or picture and a deepfake

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anymore.

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How can we tell what's real from what isn't?

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How can audio files or video files be used in court as proof if an AI can entirely generate

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them?

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Well, this new paper may provide answers to these questions.And the answer here may again

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be the use of artificial intelligence.

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The saying "I'll believe it when I'll see it" may soon change for "I'll believe it when

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the AI tells me to believe it..."

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I will assume that you've all seen deepfakes and know a little about them.

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Which will be enough for this article.

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For more information about how they are generated, I invite you to watch the video I made explaining

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deepfakes just below, as this video will focus on how to spot them.

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More precisely, I will cover a new paper by the USA DEVCOM Army Research Laboratory entitled

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"DEFAKEHOP: A LIGHT-WEIGHT HIGH-PERFORMANCE DEEPFAKE DETECTOR."

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Indeed, they can detect deepfakes with over 90% accuracy in all datasets and even reach

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100% accuracy in some benchmark datasets.

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What is even more incredible is the size of their detection model.

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As you can see, this DeFakeHop model has merely 40 thousand parameters, whereas the other

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techniques yielding much worse accuracy had around 20 million!

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This means that their model is 500 HUNDRED times smaller while outperforming the previous

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state-of-the-art techniques.

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This allows the model to quickly run on your mobile phone and allows you to detect deep

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fakes anywhere.

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You may think that you can tell the difference between a real picture or a fake one, but

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if you remember the study I shared a couple of weeks ago, it clearly showed that around

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50 percent of participants failed.

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It was basically a random guess on whether a picture was fake or not.

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There is a website from MIT where you can test your ability to spot deefakes if you'd

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like to.

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Having tried it myself, I can say it's pretty fun to do.

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There are audio files, videos, pictures, etc.

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The link is in the description below.

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If you try it, please let me know how well you do!

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And if you know any other fun apps to test yourself or help research by trying our best

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to spot deepfakes, please link them in the comments.

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I'd love to try them out!

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Now, if we come back to the paper able to detect them much better than we can, the question

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is: how is this tiny machine learning model able to achieve that while humans can't?

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DeepFakeHop works in four steps.

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Step 1:

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At first, they use another model to extract 68 different facial landmarks from each video

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frame.

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These 68 points are extracted to understand where the face is, recenter, orient and resize

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it to make them more consistent, and then extract specific parts of the face from the

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image.

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These are the "patches" of the image we will send our network, containing specific individual

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face features like the eyes, mouth, nose.

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It is done using another model called OpenFace 2.0.

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It can accurately perform facial landmark detection, head pose estimation, facial action

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unit recognition, and eye-gaze estimation in real-time.

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These are all tiny patches of 32 by 32 that will all be sent into the actual network one

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by one.

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This makes the model super efficient because it deals with only a handful of tiny images

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instead of the full image.

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More details about OpenFace2.0 can be found in the references below if you are curious

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about it.

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Step 2 to 4 (left to right, blue, green, orange):

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More precisely, the patches are sent to the first PixelhHop++ unit named Hop-1, as you

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can see.

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Representing the step one in blue.

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This is an algorithm called Saab transform to reduce the dimension.

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It will take the 32 by 32 image and reduce it to a downscaled version of the image but

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with multiple channels representing its response from different filters learned from the Saab

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transform.

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You can see the Saab transform as a convolution process, where the kernels are found using

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the PCA dimension reduction algorithm replacing the need of backpropagation to learn these

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weights.

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I will come back to the PCA dimension reduction algorithm in a minute as it is repeated in

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the next stage.

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These filters are optimized to represent the different frequencies in the image, basically

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getting activated by varying degrees of details.

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The Saab transform was shown to work well against adversarial attacks compared to basic

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convolutions trained with backpropagation.

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You can also find more information about the Saab transformation in the references below.

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If you are not used to how convolutions work, I strongly invite you to watch the video I

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made introducing them:

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I said Saab transforms worked well on adversarial attacks.

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These adversarial attacks happen when we "attack" an image by changing a few pixels or adding

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noise that humans cannot see to change the results of a machine learning model processing

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the image.

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So to simplify, we can basically see this PixelHop++ Unit as a typical 3 by 3 convolution

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here since we do not look at the training process.

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Of course, it works a bit differently, but it will make the explanation much more straightforward

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as the process is comparable.

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Then, the "Hop" step is repeated three times to get smaller and smaller versions of the

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image with concentrated general information and more channels.

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These channels are simply the outputs, or responses, of the input image by filters that

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react differently depending on the level of detail in the image, as I said earlier.

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One new channel per filter used.

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Thus, we obtain various results giving us precise information about what the image contains,

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but these results are smaller and smaller containing less spatial details unique to

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that precise image sent in the network, and therefore have more general and useful information

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with regard to what the image actually contains.

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The first few images are still relatively big, starting at 32 by 32, being the initial

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size of the patch and thus contains all the details.

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Then, it drops to 15 by 15, and finally to 7 by 7 images, meaning that we have close

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to zero spatial information in the end.The 15 by 15 image will just look like a blurry

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version of the initial image but still contains some spatial information, while the 7 by 7

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image will basically be a very general and broad version of the image with close to no

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spatial information at all.

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So just like a convolutional neural network, the deeper we get, the more channels we have

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meaning that we have more filter responses reacting to different stimuli, but the smaller

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they each are, ending with images of size 5x5.

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Allowing us to have a broader view in many ways, keeping a lot of unique valuable information

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even with smaller versions of the image.

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The images get even smaller because each of the PixelHop units is followed by a max-pooling

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step.

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They are simply taking the maximum value of each square of two by two pixels, reducing

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the image size by a factor of four at each step.

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Then, as you can see in the full model shown above, the outputs from each max-pooling layer

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are sent for further dimension reduction using the PCA algorithm.

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Which is the third step, in green.

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The PCA algorithm mainly takes the current dimensions, for example, 15 by 15 here in

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the first step, and minimizes that while maintaining at least 90% of the intensity of the input

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image.

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Here is a very simple example of how PCA can reduce the dimension, where two-dimensional

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points of cats and dogs are reduced to one dimension on a line, allowing us to add a

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threshold and easily build a classifier.

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Each hop gives us respectively 45, 30, and 5 parameters per channel instead of having

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images of size 15 by 15, 7 by 7, and 3 by 3, which would give us in the same order 225,

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49, and 9 parameters.

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This is a much more compact representation while maximizing the quality of information

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it contains.

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All these steps were used to compress the information and make the network super fast.

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You can see this as squeezing all the helpful juice at different levels of details of the

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cropped image to finally decide whether it is fake or not, using both detailed and general

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information in the decision process (step 4 in orange).

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I'm glad to see that the research in countering these deepfakes is also advancing, and I'm

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excited to see what will happen in the future with all that.

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Let me know in the comments what you think will be the main consequences and concerns

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regarding deepfakes.

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Is it going to affect law, politics, companies, celebrities, ordinary people?

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Well, pretty much everyone...

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Let's have a discussion to share awareness and spread the word to be careful and that

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we cannot believe what we see anymore, unfortunately.

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This is both an incredible and dangerous new technology.

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Please, do not abuse this technology and stay ethically correct.

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The goal here is to help improve this technology and not to use it for the wrong reasons.