Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
Book Image

Modern Computer Vision with PyTorch

By: V Kishore Ayyadevara, Yeshwanth Reddy

Overview of this book

Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.
Table of Contents (25 chapters)
Section 1 - Fundamentals of Deep Learning for Computer Vision
Section 2 - Object Classification and Detection
Section 3 - Image Manipulation
Section 4 - Combining Computer Vision with Other Techniques

Performing an adversarial attack on images

In the previous section, we learned about generating an image from random noise using a VAE. However, it was an unsupervised exercise. What if we want to modify an image in such a way that the change in image is so minimal that it is indistinguishable from the original image for a human, but still the neural network model perceives the object as belonging to a different class? Adversarial attacks on images come in handy in such a scenario.

Adversarial attacks refer to the changes that we make to input image values (pixels) so that we meet a certain objective.

In this section, we will learn about modifying an image slightly in such a way that the pre-trained models now predict them as a different class (specified by the user) and not the original class. The strategy we will adopt is as follows:

  1. Provide an image of an elephant.
  2. Specify the target class corresponding to the image.
  3. Import a pre-trained model where the parameters of the model are...