Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, in which it is difficult to distinguish between real and fake features. This technology has greatly advanced and promotes a wide range of applications in TV channels, video game industries, and cinema, such as improving visual effects in movies, as well as a variety of criminal activities, such as misinformation generation by mimicking famous people. To identify and classify DeepFakes, research in DeepFake detection using deep neural networks (DNNs) has attracted increased interest. Basically, DeepFake is the regenerated media that is obtained by injecting or replacing some information within the DNN model. In this survey, we will summarize the DeepFake detection methods in face images and videos on the basis of their results, performance, methodology used and detection type. We will review the existing types of DeepFake creation techniques and sort them into five major categories. Generally, DeepFake models are trained on DeepFake datasets and tested with experiments. Moreover, we will summarize the available DeepFake dataset trends, focusing on their improvements. Additionally, the issue of how DeepFake detection aims to generate a generalized DeepFake detection model will be analyzed. Finally, the challenges related to DeepFake creation and detection will be discussed. We hope that the knowledge encompassed in this survey will accelerate the use of deep learning in face image and video DeepFake detection methods.

Attributed networks are prevalent in the current information infrastructure, where node attributes enhance knowledge discovery. Anomaly detection in attributed networks is gaining attention for its potential uses in cybersecurity, finance, and healthcare. Recognizing the complicated relationship between node attributes and network topology is crucial for attributed network embedding and anomaly detection. Nevertheless, there are few approaches available to directly represent the relationship between these two perspectives of the node property and the network topology. Approaches utilizing the reconstruction error rely on straightforward, simple mappings, which introduce a substantial risk of overfitting in high-dimensional data, wherein the model acquires patterns that are exclusive to the training data and fails to generalize to new data. To do this, we suggest a new way to find graph anomalies on attributed networks using random masking and padding along with sparse canonical correlation analysis. Motivated by the limitations of existing methodologies in effectively addressing these challenges, our research introduces a novel methodology for anomaly detection in attributed networks by leveraging Sparse Canonical Correlation Analysis (SCCA) in conjunction with Random Masking and Padding (RMP). This dual approach uniquely addresses the challenges of high-dimensional data and the sparsity of attributes, which are prevalent issues in anomaly detection. Unlike previous works that primarily focus on either dimensionality reduction or attribute sparsity independently, our method synergizes these aspects to enhance detection performance. Initially, we randomly mask and pad nodes in the attributed network and use the Graph Convolutional Network (GCN) to map them to latent space. Next, we optimize the distribution alignment of node attributes and graph structure latent space representations by using Kullback-Leibler (KL) divergence regularization, which increases their comparability. Finally, we use sparse canonical correlation analysis (SCCA) to quantify the correlation between node attributes and network structure views in latent space. SCCA incorporates sparsity by making the model choose fewer variables, which adds another level of complexity. It improves interpretability and reduces overfitting in high-dimensional data analysis by highlighting only the key variables. To optimize our model, we maximize the correlation between attribute and structural aspects of normal nodes, and anomalies are detected by measuring the correlation between these two views. Our approach is the first of its kind to provide a novel remedy to the fundamental problems preventing efficient and accurate anomaly identification, thereby establishing a new standard in this field. The proposed model has been extensively tested on four real-world datasets, and its effectiveness has been demonstrated in comparison to state-of-the-art approaches. The empirical evaluation across multiple benchmark datasets validates the potential of the proposed approach as a pivotal tool in advancing anomaly detection research and applications.

Reversible data hiding in encrypted image (RDHEI) is advantageous to scenarios where complete recovery of the original cover image and additional data are required. In some of the existing RDHEI schemes, the image pre-processing step involved is an overhead for the resource-constrained devices on the sender’s side. In this paper, an efficient separable reversible data hiding scheme over a homomorphically encrypted image that assures privacy preservation of the contents in the cloud environment is proposed. This proposed scheme comprises three stakeholders: content-owner, data hider, and receiver. Initially, the content-owner encrypts the original image and sends the encrypted image to the data hider. The data hider embeds the encrypted additional data into the encrypted image and then sends the marked encrypted image to the receiver. On the receiver’s side, both additional data and the original image are extracted in a separable manner, i.e., additional data and the original image are extracted independently and completely from the marked encrypted image. The present scheme uses public key cryptography and facilitates the encryption of the original image on the content-owner side, without any pre-processing step involved. In addition, our experiment used distinct images to demonstrate the image-independency and the obtained results show high embedding rate where the peak signal noise ratio (PSNR) is +∞ dB for the directly decrypted image. Finally, a comparison is drawn, which shows that the proposed scheme is an optimized approach for resource-constrained devices as it omits the image pre-processing step.

The Dark Web is a difficult and anonymous network used by cybercriminals, terrorists, and state-sponsored agents to carry out their illicit goals. Dark web cybercrime is very similar to offline crime. However, the vastness of the Dark Web, its unpredictable ecology, and the anonymity it provides are all obstacles that must be overcome in order to track down criminals. To reach the dark web, which is not indexed by search engines, you must use the anonymous Tor browser. The anonymity and covert nature of the network make it ideal for criminal activity and the launch of carefully orchestrated, malicious assaults. Online criminal activity is rampant and getting more intense, according to specialists in cyber security. This chapter has provided a thorough analysis of the various attacks and attack strategies utilized on the dark web. In addition, the authors examine the strengths and weaknesses of the various methods currently in use for threat detection, and how they apply to anonymity networks such as Tor, I2P, and Freenet.

Reversible Data Hiding in Encrypted Images (RDH-EI) has gained much popularity in the field of signal processing and cloud computing. In this study, we propose a high-capacity RDH-EI scheme using multi-layer embedding. It ensures that after extraction of the embedded additional data from the marked encrypted image, the original image is recovered completely. Broadly, the method comprises three parties: the content owner, the data hider/ cloud owner, and the receiver. In the beginning, the image is encrypted by the content owner using encryption based on the chaotic behaviour of PWL-memristor and sent to the data hider. At the data hider’s side, even-odd value embedding technique is applied in multiple layers of the encrypted image to increase the embedding capacity. Meanwhile, the auxiliary information consisting of location map (LM) for each layer and the number of layers, to be used in the recovery phase, is recorded during the embedding. After completing the embedding procedure, permutation operation is applied on the marked encrypted image to prevent the perceptual information leakage. Finally, at the receiver’s side the embedded additional data and the original image is recovered losslessly with the help of valid keys and shared auxiliary information. From the experimental results, it is observed that the quality of the directly decrypted image is 51.0 dB or above. The advantage of the proposed scheme is that the PSNR is almost same for each layer, i.e. even for the n number of layers used, the PSNR is 51.0 dB or above. Moreover, we applied chosen plaintext attack to test the encryption function and in case of any cropping attack on the marked encrypted image during the transfer, the cover image can be recovered with less distortion.