Masafumi HAGIWARA’s research while affiliated with Keio University and other places

Adversarial examples can be used to exploit vulnerabilities in neural networks and threaten their sensitive applications. Adversarial attacks are evolving daily, and are rapidly rendering defense methods that assume specific attacks obsolete. This paper proposes a new defense method that does not assume a specific adversarial attack, and shows that it can be used efficiently to protect a network from a variety of adversarial attacks. Adversarial perturbations are small values; consequently, an image quality recovery method is considered to be an effective way to remove adversarial perturbations because such a method often includes a smoothing effect. The proposed method, called the denoising-based perturbation removal network (DPRNet), aims to eliminate perturbations generated by an adversarial attack for image classification tasks. DPRNet is an encoder–decoder network that excludes adversarial images during training and can reconstruct a correct image from an adversarial image. To optimize DPRNet’s parameters for eliminating adversarial perturbations, we also propose a new perturbation removal loss (PRloss) metric, which consists of a reconstructed loss and a Kullback–Leibler divergence loss that expresses the class probability distribution difference between an original image and a reconstructed image. To remove adversarial perturbation, the proposed network is trained using various types of distorted images considering the proposed PRloss metric. Thus, DPRNet eliminates image perturbations, allowing the images to be classified easily. We evaluate the proposed method using the MNIST, CIFAR-10, SVHN, and Caltech 101 datasets and show that the proposed defense method invalidates 99.8%, 95.1%, 98.7%, and 96.0% of the adversarial images that are generated by several adversarial attacks in the MNIST, CIFAR-10, SVHN, and Caltech 101 datasets, respectively.

Current image quality assessment (IQA) methods require the original images for evaluation. However, recently, IQA methods that use machine learning have been proposed. These methods learn the relationship between the distorted image and the image quality automatically. In this paper, we propose an IQA method based on deep learning that does not require a reference image. We show that a convolutional neural network with distortion prediction and fixed filters improves the IQA accuracy.

This paper proposes a quality recovery network (QRNet) that recovers the image quality from distorted images and improves the classification accuracy for image classification using these recovered images as the classifier inputs, which are optimized for image quality loss and classification loss. In certain image classification tasks, classifiers based on deep neural networks achieve higher performance compared to those realized by humans. However, these tasks are based on images that are not distorted. To address distorted images, the classifier is fine-tuned with distorted images for practical applications. However, fine-tuning is insufficient for classifying images that include multiple distortion types with severe distortions and often requires the classifier to be retrained for adapting to distorted images, which is a time-consuming process. Therefore, we propose QRNet that generates recovered images for input to the classifier. To address multiple severe distortions, the proposed network is trained using multiple distortion-type images with our proposed loss, which comprises the image quality and classification losses. Moreover, by training the proposed network with multiple classifiers, the recovered images can be easily classified by a new classifier that is not used for training. The new classifier can classify the recovered images without retraining for adapting to distorted images. We evaluate our proposed network with classifiers on public datasets and demonstrate that it improves the classification accuracy for distorted images. Moreover, the experimental results demonstrate that our proposed network with the new classifier improves the classification accuracy.