Convolutional neural networkThe existing traditional network attack detection algorithms have low detection accuracy and are difficult to cope with external malicious and capricious network attacks in the commu
Convolutional Neural Network Adversarial Attacks Note: I am aware that there are some issues with the code, I will update this repository soon (Also will move away from cv2 to PIL). This repo is a branch off ofCNN Visualisationsbecause it was starting to get bloated. It contains following ...
Note that when the norm for adversarial examples becomes larger than a threshold, the model accuracy might rise, probably because a larger norm leading to a larger optimization iteration step might cause the attack methods, including FGSM and PGD, fail to find the optima. Previously proposed ...
Nowadays, convolutional neural network (CNN) based steganalysis methods achieved great performance. While those methods are also facing security problems. In this paper, we proposed an attack scheme aiming at CNN based steganalyzer including two different attack methods 1) the LSB-Jstego Gradient Based...
This is known as attack methods. In order to defend the model against the de- ception, another group of researchers proposed a wide range of methods (known as defense methods) [1, 38, 44, 45, 24]. These are but a few highlights among a...
In the dying minutes of the match, by initiating a swift counter-attack, the Brazilian center-forward Tostão orchestrated a through pass to Pelé, the player widely regarded as the best in history. Racing up the center, the football icon found himself face to face with the Uruguayan ...
The training losses of all network models. Full size image From the perspective of recognition accuracy, network models can obtain the higher accuracy than the SVM, and using CNN model is better than other methods in this experiment. After introducing the basic WOA to optimize the parameters of...
• Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. • Convolutional neural network is composed of multiple building blocks, such as convolution lay...
Illustration of the proposed replay attack detection neural network. The dark rectangle represents variable convolution, and the three internal parameters refer to the parameters of the CNN layer on the right side of Figure 2. First, the convolution kernel size is set to 5 by 5, and the step...
First, we evaluate the performance of different CNN models using two different methods of data input presentation and data input splitting. Second, we study the impact of the different CNN hyper-parameters on the attack detection rate. Third, we select the best deep learning toolbox that will ...