An Adaptive and Secure Holographic Image Watermarking Scheme

Text information security protection method based on computer-generated holograms

Currently, computer-generated holograms (CGHs) based on ray tracing technology are generated faster and faster, and the reconstructed scenes are getting bigger and bigger and contain more and more information. Based on this situation, there are also more applications of using CGHs to hide information, but there is a lack of research on the ability to hide information. To address this issue, this paper proposes a point-sampling CGH method based on ray tracing. Our method utilizes ray tracing techniques to rapidly sample text information at different depths in the scene and hides the depth-encoded text information in the carrier image using discrete cosine transform. The reconstructed image after embedding shows good results, with a peak signal-to-noise ratio (PSNR) of 29.56 dB between the hidden images before and after embedding. The PSNR value between the embedded carrier image and the original carrier image is 51.66 dB, making it difficult for the human eye to distinguish, thereby effectively protecting the generated CGH. We also analyzed the maximum information density and observed that computational holograms obtain the maximum information density at 200×200 resolution.

Optical Encryption Based on Computer Generated Holograms in Photopolymer

An optical encryption method based on computer generated holograms printing of photopolymer is presented. Fraunhofer diffraction is performed based on the Gerchberg-Saxton algorithm, and a hologram of the Advanced Encryption Standard encrypted Quick Response code is generated to record the ciphertext. The holograms of the key and the three-dimensional image are generated by the angular spectrum diffraction algorithm. The experimental results show that large-size encrypted Quick Response (QR) code and miniature keys can be printed in photopolymers, which has good application prospects in optical encryption. This method has the advantages of high-density storage, high speed, large fault tolerance, and anti-peeping.

Digital Image Watermarking Techniques: A Review

Digital image authentication is an extremely significant concern for the digital revolution, as it is easy to tamper with any image. In the last few decades, it has been an urgent concern for researchers to ensure the authenticity of digital images. Based on the desired applications, several suitable watermarking techniques have been developed to mitigate this concern. However, it is tough to achieve a watermarking system that is simultaneously robust and secure. This paper gives details of standard watermarking system frameworks and lists some standard requirements that are used in designing watermarking techniques for several distinct applications. The current trends of digital image watermarking techniques are also reviewed in order to find the state-of-the-art methods and their limitations. Some conventional attacks are discussed, and future research directions are given.

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors

In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain. The chaotic system is extremely robust and consists of a strong chaotic generator and a 2-D Cat map. Its main role is to secure the content of a message in case a message is detected. Secondly, three blind steganalysis methods, based on multi-resolution wavelet decomposition, are used to detect whether an embedded message is hidden in the tested image (stego image) or not (cover image). The steganalysis approach is based on the hypothesis that message-embedding schemes leave statistical evidence or structure in images that can be exploited for detection. The simulation results show that the Support Vector Machine (SVM) classifier and the Fisher Linear Discriminant (FLD) cannot distinguish between cover and stego images if the message size is smaller than 20% in the EEALSBMR steganographic method and if the message size is smaller than 15% in the EDCT steganographic method. However, SVM and FLD can distinguish between cover and stego images with reasonable accuracy in the EDWT steganographic method, irrespective of the message size.