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Register a new userReview on Optical Image Hiding and Watermarking Techniques
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Added by
Shuming Jiao
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Information security is a critical issue in modern society and image watermarking can effectively prevent unauthorized information access. Optical image watermarking techniques generally have advantages of parallel high-speed processing and multi-dimensional capabilities compared with digital approaches. This paper provides a comprehensive review on the research works related to optical image hiding and watermarking techniques conducted in the past decade. The past research works are focused on two major aspects, various optical systems for image hiding and the methods for embedding optical system output into a host image. A summary of the state-of-the-art works is made from these two perspectives.
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Data hiding is the process of embedding information into a noise-tolerant signal such as a piece of audio, video, or image. Digital watermarking is a form of data hiding where identifying data is robustly embedded so that it can resist tampering and be used to identify the original owners of the media. Steganography, another form of data hiding, embeds data for the purpose of secure and secret communication. This survey summarises recent developments in deep learning techniques for data hiding for the purposes of watermarking and steganography, categorising them based on model architectures and noise injection methods. The objective functions, evaluation metrics, and datasets used for training these data hiding models are comprehensively summarised. Finally, we propose and discuss possible future directions for research into deep data hiding techniques.
Security and memory management are the major demands for electronics devices like ipods, cell phones, pmps, iphones and digital cameras. In this paper, we have suggested a high level of security mechanism by considering the concept of steganography along with the principle of cryptography. Four different methods that can save a considerable amount of memory space have been discussed. Based on these methods, we have constructed secured stego image creator and secured multi image viewer in Microsoft platform so as to provide high level of security and using less memory space for storage of image files in the above said electronic devices
In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies.
The recent advent in the field of multimedia proposed a many facilities in transport, transmission and manipulation of data. Along with this advancement of facilities there are larger threats in authentication of data, its licensed use and protection against illegal use of data. A lot of digital image watermarking techniques have been designed and implemented to stop the illegal use of the digital multimedia images. This paper compares the robustness of three different watermarking schemes against brightness and rotation attacks. The robustness of the watermarked images has been verified on the parameters of PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error) and MAE (Mean Absolute Error).
Data hiding is the art of concealing messages with limited perceptual changes. Recently, deep learning has provided enriching perspectives for it and made significant progress. In this work, we conduct a brief yet comprehensive review of existing literature and outline three meta-architectures. Based on this, we summarize specific strategies for various applications of deep hiding, including steganography, light field messaging and watermarking. Finally, further insight into deep hiding is provided through incorporating the perspective of adversarial attack.
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