Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction

Abstract

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one pixel of an image, and attacks that change brightness values within a rectangular area. MNIST and Kaggle dogs vs. cats datasets were chosen. Recognition characteristics were obtained for the accuracy, depending on the number of images subjected to attacks and the types of attacks used in the training. The study was based on well-known convolutional neural network architectures used in pattern recognition tasks, such as VGG-16 and Inception_v3. The dependencies of the recognition accuracy on the parameters of visual attacks were obtained. Original methods were proposed to prevent visual attacks. Such methods are based on the selection of “incomprehensible” classes for the recognizer, and their subsequent correction based on neural network inference with reduced image sizes. As a result of applying these methods, gains in the accuracy metric by a factor of 1.3 were obtained after iteration by discarding incomprehensible images, and reducing the amount of uncertainty by 4–5% after iteration by applying the integration of the results of image analyses in reduced dimensions.

Hierarchical Concept Learning by Fuzzy Semantic Cells

“…They can serve as an important inductive bias for out-of-distribution samples or regularize the model to avoid potential overfitting. The virtue that learning concepts from data can prevent adversarial attacks is also discussed [40]. Our method is based on the assumption of prototype theory [11] and the hierarchical structure of prototypes and concepts [24,25].…”

AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval

“…In the case of training DCNN models with scarce data, augmentation can effectively extend the performance of DCNN classifiers, avoid overtraining [ 21 ], and reduce the possibility of visual attack [ 22 ]. Various mathematical models can augment models, which can generate close to real signals and images [ 23 , 24 ].…”

Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks

“…For instance, in the specific problem of image category labeling, unsupervised deep learning approaches have been found to be powerful [23]. Many other attacks on the problem are possible, including hidden Markov models with Bayesian expectationmaximization [24,25] and dimensionality reduction with PCA [26], among others [27]. We investigate minimizing the energy flow objective function (Definition 1) over unlabeled data sets to obtain Hopfield networks that cluster them.…”