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Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times. Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic. We are using Deep Convolutional Generative Adversarial Networks (DCGAN) to trick the malware classifier to believe it is a normal entity. In this work, a new dataset is created to fool the Artificial Intelligence (AI) based malware detectors, and it consists of different types of attacks such as Denial of Service (DoS), scan 11, scan 44, botnet, spam, User Datagram Portal (UDP) scan, and ssh scan. The discriminator used in the DCGAN discriminates two different attack classes (anomaly and synthetic) and one normal class. The model collapse, instability, and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm (AO-MBHS). This algorithm helps the generator to create realistic malware samples to be undetected by the discriminator. The performance of the proposed methodology is evaluated using different performance metrics such as training time, detection rate, F-Score, loss function, Accuracy, False alarm rate, etc. The superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection system. The support vector machines (SVM) is used as the malicious traffic detection application and its True positive rate (TPR) goes from 80% to 0% after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples. [ABSTRACT FROM AUTHOR]

Existing style transfer methods have achieved great success in artwork generation by transferring artistic styles onto everyday photographs while keeping their contents unchanged. Despite this success, these methods have one inherent limitation: they cannot produce newly created image contents, lacking creativity and flexibility. On the other hand, generative adversarial networks (GANs) can synthesise images with new content, whereas cannot specify the artistic style of these images. The authors consider combining style transfer with convolutional GANs to generate more creative and diverse artworks. Instead of simply concatenating these two networks: the first for synthesising new content and the second for transferring artistic styles, which is inefficient and inconvenient, they design an end‐to‐end network called ArtistGAN to perform these two operations at the same time and achieve visually better results. Moreover, to generate images of higher quality, they propose the bi‐discriminator GAN containing a pixel discriminator and a feature discriminator that constrain the generated image from pixel level and feature level, respectively. They conduct extensive experiments and comparisons to evaluate their methods quantitatively and qualitatively. The experimental results verify the effectiveness of their methods.

Background: The objective of this study was to propose an optimal input image quality for a conditional generative adversarial network (GAN) in T1-weighted and T2-weighted magnetic resonance imaging (MRI) images. Materials and methods: A total of 2,024 images scanned from 2017 to 2018 in 104 patients were used. The prediction framework of T1-weighted to T2-weighted MRI images and T2-weighted to T1-weighted MRI images were created with GAN. Two image sizes (512 × 512 and 256 × 256) and two grayscale level conversion method (simple and adaptive) were used for the input images. The images were converted from 16-bit to 8-bit by dividing with 256 levels in a simple conversion method. For the adaptive conversion method, the unused levels were eliminated in 16-bit images, which were converted to 8-bit images by dividing with the value obtained after dividing the maximum pixel value with 256. Results: The relative mean absolute error (rMAE) was 0.15 for T1-weighted to T2-weighted MRI images and 0.17 for T2-weighted to T1-weighted MRI images with an adaptive conversion method, which was the smallest. Moreover, the adaptive conversion method has a smallest mean square error (rMSE) and root mean square error (rRMSE), and the largest peak signal-to-noise ratio (PSNR) and mutual information (MI). The computation time depended on the image size. Conclusions: Input resolution and image size affect the accuracy of prediction. The proposed model and approach of prediction framework can help improve the versatility and quality of multi-contrast MRI tests without the need for prolonged examinations. [ABSTRACT FROM AUTHOR]

Existing style transfer methods have achieved great success in artwork generation by transferring artistic styles onto everyday photographs while keeping their contents unchanged. Despite this success, these methods have one inherent limitation: they cannot produce newly created image contents, lacking creativity and flexibility. On the other hand, generative adversarial networks (GANs) can synthesise images with new content, whereas cannot specify the artistic style of these images. The authors consider combining style transfer with convolutional GANs to generate more creative and diverse artworks. Instead of simply concatenating these two networks: the first for synthesising new content and the second for transferring artistic styles, which is inefficient and inconvenient, they design an end‐to‐end network called ArtistGAN to perform these two operations at the same time and achieve visually better results. Moreover, to generate images of higher quality, they propose the bi‐discriminator GAN containing a pixel discriminator and a feature discriminator that constrain the generated image from pixel level and feature level, respectively. They conduct extensive experiments and comparisons to evaluate their methods quantitatively and qualitatively. The experimental results verify the effectiveness of their methods. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to propose an optimal input image quality for a conditional generative adversarial network (GAN) in T1-weighted and T2-weighted magnetic resonance imaging (MRI) images., Materials and Methods: A total of 2,024 images scanned from 2017 to 2018 in 104 patients were used. The prediction framework of T1-weighted to T2-weighted MRI images and T2-weighted to T1-weighted MRI images were created with GAN. Two image sizes (512 × 512 and 256 × 256) and two grayscale level conversion method (simple and adaptive) were used for the input images. The images were converted from 16-bit to 8-bit by dividing with 256 levels in a simple conversion method. For the adaptive conversion method, the unused levels were eliminated in 16-bit images, which were converted to 8-bit images by dividing with the value obtained after dividing the maximum pixel value with 256., Results: The relative mean absolute error (rMAE ) was 0.15 for T1-weighted to T2-weighted MRI images and 0.17 for T2-weighted to T1-weighted MRI images with an adaptive conversion method, which was the smallest. Moreover, the adaptive conversion method has a smallest mean square error (rMSE) and root mean square error (rRMSE), and the largest peak signal-to-noise ratio (PSNR) and mutual information (MI). The computation time depended on the image size., Conclusions: Input resolution and image size affect the accuracy of prediction. The proposed model and approach of prediction framework can help improve the versatility and quality of multi-contrast MRI tests without the need for prolonged examinations., Competing Interests: Conflict of interest None declared., (© 2021 Greater Poland Cancer Centre.)