The new age of machines has restructured our entertainment purposes and means dramatically. And why not? Adapting and developing cognitive abilities as humans is a trusted reality.
To add to it, Face Swapping has been popularized over the past few years. Imagining yourself as a celebrated personality is a business of a few clicks and taps. But the back of this tech wonder is a bit tangled.
While there are only definite methods of creating photorealistic face swap videos, Convolutional Neural Network(CNN) is the eye of AI-deepfake businesses.
But how to define CNN’s reliability without any primitive understanding of its working? So, let’s get started with the basics.
Titbits of CNN
Convolutional Neural Network or ConvNets is every tech-elite’s favorite deep learning architecture which uses image segmentation to extract the features rather than considering it as a whole.
This sophisticated network of neuron nodes can be trained to learn patterns on its own after providing initial training sets.
And hence, blooming tech-industries of Image Processing, Speech Recognition, Signal Processing are heavily leaned on CNN for both perfection and quality. From analyzing geographical satellite images to identifying objects, these networks always pass the litmus test of accuracy.
A Convolutional Neural Network divides an input image or channel into blocks and forms a matrix of pixels. At the core of the operations of CNN, rests a kernel which is again nothing but a matrix containing randomized values or numbers that slide or, to jargonize, convolute across each block of the input. Simple may it seem, but the real procedure is way intricate.
A ConvNet or CNN contains multiple convolutional layers, each containing predefined numbers of filters.
Then what is a filter? Let’s split the network further.
A ConvNet contains at least 3 input channels(RGB) and for each channel, there will be a dedicated and unique kernel. An individual kernel slides over a slab of pixels representing a smaller part of the face or image. It could be anything from the corners of an eye to the lines of a lip. It conducts dot product of the overlapped part of input matrix and kernel. For the other channels, the same method is followed and the results are then concatenated using a bias. And it generates a compressed output known as a filter. This outcome is then fed to the next layer as an input for perceiving more features and shapes.
The job of a filter is to detect the edges, circles, lines, or any details in the image. It can even recognize the eyes and nose of an animal or a whole landscape without losing any precision. And hence, the same technique is put in for face recognition and face swap technologies.
But does this merit-performance assure a faster output too? Or is there a trade-off? Let’s find out.
Faster than your gesture
The answer to why many modern industries rely upon ConvNet is not a covert affair. Unlike Multi Layer Perceptron(MLP), Convolutional Neural Network is not fully connected. CNN eliminates the trouble of coupling each node of the layer with every other one. It reuses and shares the weights between blocks rather than one-to-one assignment.
To understand this in pieces and bits, here is a small assumption case:
In the case of MLP, if the input has 5×5=25 features and the kernel has 3×3=9 individual values, the output will have 25×9=225 parameters. But ConvNet reduces the need for parameters by equalizing the depth of input and kernel and thus flattening the output layer. In the latter case, the parameter yield will be only 9 with a stalk of 1. Decreasing the parameters will be directly reflected as computational power reduction. And that’s the science behind CNN-based faster face swaps.
ConvNet exhibits this effectiveness the deeper we go into the convolutional process. It finds and matches the patterns regardless of the placement of the entity.
To save a face swap video from being distorted, many deep layers of face recognition are used. The more layers the more complex is the algorithm and the more resources are needed. And any small anomaly ignored in the beginning layer becomes a bigger quality bug as we proceed ahead deeper into the network.
But ConvNet still never fails to produce a result that makes it hard to guess which one is the synthetic video. Have a closer look at its error-free mechanism.
Acing with Accuracy
A convolutional layer is often followed by a maximum pooling layer. To simply put, a filter of the ConvNet is divided into a group of pixel blocks and then the maximum value out of that section is picked up. Let’s take a small example.
Given below is an output filter of 4× 4 with a stride of 2. In the first section, colored in green, the highest value 15 is pooled out and the same procedure is being applied to the other colored blocks as well. And thus the final output is a 2×2 matrix. In reality, there are numerous filters and each and every filter consists of hundreds of variables.
That’s how maximum pooling shines as a quality enhancer.
It can denoise the ConvNet’s out-turn by focusing on desired features and borders.
To imitate the exact expressions, recreate a face without any loss of edges and sharp profiles, and on-the-nail face detection never gets looked out by this pooling formula.
Convolutional Neural Network is a pivot of the field of computer vision. Today’s world and its people never settle for even a minor loophole. And with everything pacing up at a rocket-speed, ConvNet bridges the gap between timeliness and excellence. It’s transforming the deepfake industry by bringing brilliancy upfront.