Null Space Properties of Neural Networks with Applications to Image Steganography

Neural networks are powerful learning methods in use for various tasks today. This is especially true in the domain of image recognition, where neural networks can achieve even human-competitive results[13]. However, a number of studies have revealed that neural networks for image classification can be easily influenced to misclassify by modifying images[1]. In 2014, Szegedy et al. first discovered an intriguing weakness of deep neural networks[15]. They showed that neural networks for image classification can be easily fooled by small perturbations, and they called these intentionally modified images adversarial examples. Following this observation, numerous studies have been carried out to find different ways to generate adversarial examples[7, 11, 13]. The main idea is to find a subtle perturbation that can drastically change the output of a neural network by adding it to the data. It is observed that adversarial examples have good transferability across models, which suggests that the existence of adversarial examples is also a property of datasets[8], thus adversarial examples are not restricted only to the given model. In our study, we aim to find a model-based method to fool the neural networks.