A multiple-image cryptographic authentication scheme based on phase-only holograms is proposed in this paper. First, computer-generated holograms of original images to be authenticated are encoded to phase-only holograms using the Floyd-Steinberg error diffusion algorithm. Second, each phase-only hologram image is randomly sampled as the sparse representation with the help of its random binary mask. Finally, the phase-only ciphertext containing the information of original images is obtained by integrating all sparse representations using their binary masks. The existence of each original image can be verified by calculating the nonlinear correlation map between it and its corresponding decrypted result. High security level of this cryptosystem can be achieved by considering random binary masks as secret keys. This work provides an effective alternative for the related research based on computer-generated holograms.
An optical image encryption method is proposed based on Fourier single-pixel imaging and iterated phase retrieval algorithm. First, a binary barcode image containing two groups of horizontal strips is randomly generated and used as the target image in the single-pixel imaging process. Second, considering the barcode image as the amplitude constraint, the original plaintext image is encoded to two phase-only masks using a designed phase retrieval algorithm in Fresnel domain, and these masks are applied as secret keys. Finally, the barcode image is encrypted to a series of measurements using two-step phase shift Fourier single-pixel imaging as the ciphertext. Differing other methods, the original image is not directly imaged and encoded into the ciphertext. Even the ciphertext is obtained by a cracker, only the barcode image may be further discovered. Due to two phase-only masks as secret keys, the security level of the cryptosystem can be enhanced greatly. The simulated results verify the feasibility of the proposed method, and this work provides an effective alternative for the related research.
Image feature detection and matching are still important to many applications in computer vision. Light field imaging captures structured multi-view data, which provides a potential capability for solving the polysemy problem of feature matching. In this paper, we propose a light field multi-scale blocked LBP feature extraction and matching algorithm based on the light field spatial-angular domain. The proposed algorithm is on the basis of the classic local binary pattern feature, which can be enhanced by adding the description of the variations of different angular views. First, the Harris feature detection is employed to select feature candidates from the light field central sub-aperture images. Then, an orientation selection is introduced to calculate the most invariant angular neighbors. We suggest extracting the local binary pattern features of the selected angular views and stitching them together, to form a novel light field multi-scale blocked LBP feature. Finally, the distance between different features is measured using the vector cosine similarity. The proposed algorithm achieves an average matching precision of 93% in both virtual and real scenes on the paired light fields matching dataset. The proposed algorithm outperforms the classical SIFT feature and the state-of-the-art light field feature. The proposed local binary features are also significantly better than SIFT features and LiFF features in terms of descriptor length.
With the development of privacy protection, reversible data hiding methods in encrypted image have drawn extensive research interest. Among them, a new method is proposed based on embedding prediction errors, i.e., EPE-based method, where secret information is embedded in the encrypted most significant bit plane. Not only the original image can be recovered with high quality but also the payload can reach close to 1 bit per pixel (bpp). However, there are potential errors in the process of extracting secret data, because most significant bits of a part of pixels are used as flags to mark prediction error location. In this paper, a reversible data hiding method in encrypted image with high capacity is proposed by combining most significant bit prediction with least significant bit compression. At first, most significant bit of each pixel is predicted and a location map of prediction errors in the original image is generated. In the same time, the original image is encrypted using a stream cipher method. Then, the location map is embedded into the vacated space generated with compressing least significant bits and the secret data is embedded into most significant bits of a part of pixels without prediction errors. In this way, the marked encrypted image is obtained. Finally, the original image can be recovered without any error and the secret information can be extracted correctly from the marked encrypted image. Experimental results show that the proposed method has better performance than EPE-based and other methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.