Lighting and Optical Tools for Image Forensics

We present new forensic tools that are capable of detecting traces of tampering in digital images without the use of watermarks or specialized hardware. These tools operate under the assumption that images contain natural properties from a variety of sources, including the world, the lens, and the sensor. These properties may be disturbed by digital tampering and by measuring them we can expose the forgery. In this context, we present the following forensic tools: (1) illuminant direction, (2) specularity, (3) lighting environment, and (4) chromatic aberration. The common theme of these tools is that they exploit lighting or optical properties of images. Although each tool is not applicable to every image, they add to a growing set of image forensic tools that together will complicate the process of making a convincing forgery

Robust Image Watermarking Using QR Factorization In Wavelet Domain

A robust blind image watermarking algorithm in wavelet transform domain (WT) based on QR factorization, and quantization index modulation (QIM) technique is presented for legal protection of digital images. The host image is decomposed into wavelet subbands, and then the approximation subband is QR factorized. The secret watermark bit is embedded into the R vector in QR using QIM. The experimental results show that the proposed algorithm preserves the high perceptual quality. It also sustains against JPEG compression, and other image processing attacks. The comparison analysis demonstrates the proposed scheme has better performance in imperceptibility and robustness than the previously reported watermarking algorithms

Web Service Deployment for Selecting a Right Steganography Scheme for Optimizing Both the Capacity and the Detectable Distortion

The principal objective of this effort is to organize a network facility to hide the secret information in an image folder without disturbing its originality. In the literature lot of algorithms are there to hide the information in an image file but most of it consumes high resource for completing the task which is not suitable for light weight mobile devices. Few basic algorithms like 1LSB, 2LSB and 3LSB methods in the literature are suitable for mobile devices since the computational complexity is very low. But, these methods either lack in maintaining the originality of the source image or in increasing the number of bits to be fixed. Furthermore, every algorithm in the literature has its own merits and demerits and we cannot predict which algorithm is best or worst since, based on the parameters such as size of the safety duplicate and encryption algorithm used to generate the cipher text the steganography schemes may produce best or worst result with respect to computational complexity, capacity, and detectable distortion. In our proposed work, we have developed a web service that takes cover image and plain text as the input from the clients and returns the steganoimage to the clients. The steganoimage will be generated by our proposed work by analyzing the above said parameters and by applying the right steganography scheme. The proposed work helps in reducing the detectable distortion, computational complexity of the client device, and in increasing the capacity. The experimental result says that, the proposed system performs better than the legacy schemes with respect to capacity, computational complexity, and detectable distortion. This proposed work is more useful to the client devices with very low computational resource since all the computational tasks are deployed in the server side

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis