Biblio

Nowadays deepfake videos are concern with social ethics, privacy and security. Deepfake videos are synthetically generated videos that are generated by modifying the facial features and audio features to impose one person’s facial data and audio to other videos. These videos can be used for defaming and fraud. So, counter these types of manipulations and threats, detection of deepfake video is needed. This paper proposes multilayer hybrid recurrent deep learning models for deepfake video detection. Proposed models exploit the noise-based temporal facial convolutional features and temporal learning of hybrid recurrent deep learning models. Experiment results of these models demonstrate its performance over stacked recurrent deep learning models.

The accessibility of the internet and mobile platforms has risen dramatically due to digital technology innovations. Web applications have opened up a variety of market possibilities by supplying consumers with a wide variety of digital technologies that benefit from high accessibility and functionality. Around the same time, web application protection continues to be an important challenge on the internet, and security must be taken seriously in order to secure confidential data. The threat is caused by inadequate validation of user input information, software developed without strict adherence to safety standards, vulnerability of reusable software libraries, software weakness, and so on. Through abusing a website's vulnerability, introduers are manipulating the user's information in order to exploit it for their own benefit. Then introduers inject their own malicious code, stealing passwords, manipulating user activities, and infringing on customers' privacy. As a result, information is leaked, applications malfunction, confidential data is accessed, etc. To mitigate the aforementioned issues, stacking ensemble based classifier model for Cross-site scripting (XSS) attack detection is proposed. Furthermore, the stacking ensembles technique is used in combination with different machine learning classification algorithms like k-Means, Random Forest and Decision Tree as base-learners to reliably detect XSS attack. Logistic Regression is used as meta-learner to predict the attack with greater accuracy. The classification algorithms in stacking model explore the problem in their own way and its results are given as input to the meta-learner to make final prediction, thus improving the overall detection accuracy of XSS attack in stacking than the individual models. The simulation findings demonstrate that the proposed model detects XSS attack successfully.

Arbitrary style transfer refers to generate a new image based on any set of existing images. Meanwhile, the generated image retains the content structure of one and the style pattern of another. In terms of content retention and style transfer, the recent arbitrary style transfer algorithms normally perform well in one, but it is difficult to find a trade-off between the two. In this paper, we propose the Deep Content Guidance Network (DCGN) which is stacked by content guidance (CG) layers. And each CG layer involves one position self-attention (pSA) module, one channel self-attention (cSA) module and one content guidance attention (cGA) module. Specially, the pSA module extracts more effective content information on the spatial layout of content images and the cSA module makes the style representation of style images in the channel dimension richer. And in the non-local view, the cGA module utilizes content information to guide the distribution of style features, which obtains a more detailed style expression. Moreover, we introduce a new permutation loss to generalize feature expression, so as to obtain abundant feature expressions while maintaining content structure. Qualitative and quantitative experiments verify that our approach can transform into better stylized images than the state-of-the-art methods.

Due to the extraordinary volume of business information, classy cyber-attacks pointing the networks of all enterprise have become more casual, with intruders trying to pierce vast into and grasp broader from the compromised network machines. The vital security essential is that field experts and the network administrators have a common terminology to share the attempt of intruders to invoke the system and to rapidly assist each other retort to all kind of threats. Given the enormous huge system traffic, traditional Machine Learning (ML) algorithms will provide ineffective predictions of the network anomaly. Thereby, a hybridized multi-model system can improve the accuracy of detecting the intrusion in the networks. In this manner, this article presents a novel approach Clustered Generalization oriented Ensemble Learning Model (CGELM) for predicting the network anomaly. The performance metrics of the anticipated approach are Detection Rate (DR) and False Predictive Rate (FPR) for the two heterogeneous data sets namely NSL-KDD and UGR'16. The proposed method provides 98.93% accuracy for DR and 0.14% of FPR against Decision Stump AdaBoost and Stacking Ensemble methods.

In this paper, we propose a novel visual secret sharing (VSS) scheme for color QR code (VSSCQR) with (n, n) threshold based on high capacity, admirable visual effects and popularity of color QR code. By splitting and encoding a secret image into QR codes and then fusing QR codes to generate color QR code shares, the scheme can share the secret among a certain number of participants. However, less than n participants cannot reveal any information about the secret. The embedding amount and position of the secret image bits generated by VSS are in the range of the error correction ability of the QR code. Each color share is readable, which can be decoded and thus may not come into notice. On one hand, the secret image can be reconstructed by first decomposing three QR codes from each color QR code share and then stacking the corresponding QR codes based on only human visual system without computational devices. On the other hand, by decomposing three QR codes from each color QR code share and then XORing the three QR codes respectively, we can reconstruct the secret image losslessly. The experiment results display the effect of our scheme.

This paper investigates the possibility of using ensemble learning methods to improve the performance of intrusion detection systems. We compare an ensemble of three ensemble learning methods, boosting, bagging and stacking in order to improve the detection rate and to reduce the false alarm rate. These ensemble methods use well-known and different base classification algorithms, J48 (decision tree), NB (Naïve Bayes), MLP (Neural Network) and REPTree. The comparison experiments are applied on UNSW-NB15 data set a recent public data set for network intrusion detection systems. Results show that using boosting, bagging can achieve higher accuracy than single classifier but stacking performs better than other ensemble learning methods.

Biometric systems have been applied to improve the security of several computational systems. These systems analyse physiological or behavioural features obtained from the users in order to perform authentication. Biometric features should ideally meet a number of requirements, including permanence. In biometrics, permanence means that the analysed biometric feature will not change over time. However, recent studies have shown that this is not the case for several biometric modalities. Adaptive biometric systems deal with this issue by adapting the user model over time. Some algorithms for adaptive biometrics have been investigated and compared in the literature. In machine learning, several studies show that the combination of individual techniques in ensembles may lead to more accurate and stable decision models. This paper investigates the usage of some ensemble approaches to combine the output of current adaptive algorithms for biometrics. The experiments are carried out on keystroke dynamics, a biometric modality known to be subject to change over time.

Visual cryptography is a way to encrypt the secret image into several meaningless share images. Noted that no information can be obtained if not all of the shares are collected. Stacking the share images, the secret image can be retrieved. The share images are meaningless to owner which results in difficult to manage. Tagged visual cryptography is a skill to print a pattern onto meaningless share images. After that, users can easily manage their own share images according to the printed pattern. Besides, access control is another popular topic to allow a user or a group to see the own authorizations. In this paper, a self-authentication mechanism with lossless construction ability for image secret sharing scheme is proposed. The experiments provide the positive data to show the feasibility of the proposed scheme.