Imagine a deepfake video of House Speaker Nancy Pelosi, in which her speech is intentionally slurred and the words she uses are changed to deliver a message that's offensive to large numbers of voters. Now imagine that the technology used to create the video is so sophisticated that it appears completely real, rendering the manipulation undetectable, unlike clumsy deepfakes of Pelosi that circulated--and were quickly debunked--in 2020 and 2021. What would be the impact of such a video on closely contested House races in a midterm election? That's the dilemma Adobe, maker of the world's most popular tools for photo and video editing, faces as it undergoes a top-to-bottom review and redesign of its product mix using artificial intelligence and deep learning techniques. That includes upgrades to the company's signature Photoshop software and Premiere Pro video-editing tool.
I will start the course by installing Python and installing the necessary libraries in Python for developing the end-to-end project. Then I will teach you one of the prerequisites of the course that is image processing techniques in OpenCV and the mathematical concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for the images. Then we will do a mini project on Face Detection using OpenCV and Deep Neural Networks. With the concepts of image basics, we will then start our project phase-1, face identity recognition.
Deep learning uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification and others. Deep learning often uses convolutional neural networks for many or all of its layers.
For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. The progressive upscaling overcomes the limitation for previous methods in generating true colors. Finally, we use a reconstruction convolutional layer to map back the upscaled features to a high-resolution image. Our addition of an extra loss term helps in overcoming large errors, thus, generating more realistic and smooth images. We evaluate the proposed architecture on four different medical image modalities: (1) the DRIVE and STARE datasets of retinal fundoscopy images, (2) the BraTS dataset of brain MRI, (3) the ISIC skin cancer dataset of dermoscopy images, and (4) the CAMUS dataset of cardiac ultrasound images. The proposed architecture achieves superior accuracy compared to other state-of-the-art super-resolution architectures.
Though marketers are still in the early stages of experimenting with deepfakes and deepfake technology, these videos convey a more immersive marketing experience through storytelling. Deepfake technology is a type of "deep learning." Deep learning is a machine learning type that allows computers to learn tasks independently without being explicitly programmed. Deepfake technology also involves computer vision, allowing computers to recognize objects in images. For example, computer vision uses deep learning algorithms to identify objects in photos or videos.
The Terminator movie series made it clear that robots would take over our world one day and the massive emergence of technology is bringing about a disruption in the media and entertainment industry. Today we are going to take a look at Deepfake technology and how it is imparting a facelift to the media ecosystem. Deepfake is an AI-based media synthesizing technique that includes manipulating sounds and superimposing human features on another person's face/body to render a real human experience. Deep learning technology is used by the Deepfake app to imitate a person's actions, looks, and mannerisms without requiring them to be present. This results in hyper-realistic audio and video which is impossible to distinguish from the real thing.
Abstract: Substantial progress in spoofing and deepfake detection has been made in recent years. Nonetheless, the community has yet to make notable inroads in providing an explanation for how a classifier produces its output. The dominance of black box spoofing detection solutions is at further odds with the drive toward trustworthy, explainable artificial intelligence. This paper describes our use of SHapley Additive exPlanations (SHAP) to gain new insights in spoofing detection. We demonstrate use of the tool in revealing unexpected classifier behaviour, the artefacts that contribute most to classifier outputs and differences in the behaviour of competing spoofing detection models.
Artificial intelligence in general, and more specifically Deep Learning and neural networks, open the door to a new era in image processing. Why should companies look into this technology, what is important to know and how easy is it actually to set up a new project? After participation, you will have a better grasp of this new technology and be familiar with the essential know-how concerning this field. We also show you that it is actually really easy to set-up your individual, deep learning-based vision solutions, even if you have no prior knowledge.
The miseducation of algorithms is a critical problem; when artificial intelligence mirrors unconscious thoughts, racism, and biases of the humans who generated these algorithms, it can lead to serious harm. Computer programs, for example, have wrongly flagged Black defendants as twice as likely to reoffend as someone who's white. When an AI used cost as a proxy for health needs, it falsely named Black patients as healthier than equally sick white ones, as less money was spent on them. Even AI used to write a play relied on using harmful stereotypes for casting. Removing sensitive features from the data seems like a viable tweak.