Adaptive Image Watermarking based on K-NN Clustering

The key challenge faced by researchers is the rise in the use of social media communication to prove ownership rights  to  multimedia  material  such  as  video,  audio,  text,  graphics,  etc.  Watermarking  is  the  method  of multimedia concealment of digital content that can be used later to prove ownership credentials. The researchers in this field contribute a lot of work, but there is still a need for more robust methods. In this paper, we use the KNN clustering method to find the features in the image, which are then used to embed the content of the watermark.  Later,  the  KNN  clustering  approach  is  again  used  for  watermark  extraction  to  classify  the characteristics where the watermark is embedded and extraction is performed from those characteristics

Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced

A Comprehensive Review of Video Steganalysis

Steganography is the art of secret communication and steganalysis is the art of detecting the hidden messages embedded in digital media covers. One of the covers that is gaining interest in the field is video. Presently, the global IP video traffic forms the major part of all consumer Internet traffic. It is also gaining attention in the field of digital forensics and homeland security in which threats of covert communications hold serious consequences. Thus, steganography technicians will prefer video to other types of covers like audio files, still images or texts. Moreover, video steganography will be of more interest because it provides more concealing capacity. Contrariwise, investigation in video steganalysis methods does not seem to follow the momentum even if law enforcement agencies and governments around the world support and encourage investigation in this field. In this paper, we review the most important methods used so far in video steganalysis and sketch the future trends. To the best of our knowledge this is the most comprehensive review of video steganalysis produced so far

Reversible Data Hiding scheme using modified Histogram Shifting in Encrypted Images for Bio-medical images

Existing Least Significant Bit (LSB) steganography system is less robust and the stego-images can be corrupted easily by attackers. To overcome these problems Reversible data hiding (RDH) techniques are used. RDH is an efficient way of embedding confidential message into a cover image. Histogram expansion and histogram shifting are effective techniques in reversible data hiding. The embedded message and cover images can be extracted without any distortion. The proposed system focuses on implementation of RDH techniques for hiding data in encrypted bio-medical images without any loss. In the proposed techniques the bio-medical data are embedded into cover images by reversible data hiding technique. Histogram expansion and histogram shifting have been used to extract cover image and bio- medical data. Each pixel is encrypted by public key of Paillier cryptosystem algorithm. The homomorphic multiplication is used to expand the histogram of the image in encrypted domain. The histogram shifting is done based on the homomorphic addition and adjacent pixel difference in the encrypted domain. The message is embedded into the host image pixel difference. On receiving encrypted image with additional data, the receiver using his private key performs decryption. As a result, due to histogram expansion and histogram shifting embedded message and the host image can be recovered perfectly. The embedding rate is increased in host image than in existing scheme due to adjacency pixel difference

Reversible Data Hiding scheme using modified Histogram Shifting in Encrypted Images for Bio-medical images

Existing Least Significant Bit (LSB) steganography system is less robust and the stego-images can be corrupted easily by attackers. To overcome these problems Reversible data hiding (RDH) techniques are used. RDH is an efficient way of embedding confidential message into a cover image. Histogram expansion and histogram shifting are effective techniques in reversible data hiding. The embedded message and cover images can be extracted without any distortion. The proposed system focuses on implementation of RDH techniques for hiding data in encrypted bio-medical images without any loss. In the proposed techniques the bio-medical data are embedded into cover images by reversible data hiding technique. Histogram expansion and histogram shifting have been used to extract cover image and bio- medical data. Each pixel is encrypted by public key of Paillier cryptosystem algorithm. The homomorphic multiplication is used to expand the histogram of the image in encrypted domain. The histogram shifting is done based on the homomorphic addition and adjacent pixel difference in the encrypted domain. The message is embedded into the host image pixel difference. On receiving encrypted image with additional data, the receiver using his private key performs decryption. As a result, due to histogram expansion and histogram shifting embedded message and the host image can be recovered perfectly. The embedding rate is increased in host image than in existing scheme due to adjacency pixel difference

Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

Cyber Security

This open access book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: ​data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification