We present a novel method for communicating between a camera and display by embedding and recovering hidden and dynamic information within a displayed image. A handheld camera pointed at the display can receive not only the display image, but also the underlying message. These active scenes are fundamentally different from traditional passive scenes like QR codes because image formation is based on display emittance, not surface reflectance. Detecting and decoding the message requires careful photometric modeling for computational message recovery. Unlike standard watermarking and steganography methods that lie outside the domain of computer vision, our message recovery algorithm uses illumination to optically communicate hidden messages in real world scenes. The key innovation of our approach is an algorithm that performs simultaneous radiometric calibration and message recovery in one convex optimization problem. By modeling the photometry of the system using a camera-display transfer function (CDTF), we derive a physics-based kernel function for support vector machine classification. We demonstrate that our method of optimal online radiometric calibration (OORC) leads to an efficient and robust algorithm for computational messaging between nine commercial cameras and displays.
In developing a system where cameras and displays can communicate under real world conditions, the initial expectation was that existing watermarking techniques could be used directly. Certainly the work in this field is extensive and has a long history with numerous surveys compiled @cite_1 @cite_21 @cite_27 @cite_40 @cite_41 @cite_10 . Surprisingly, existing methods are not directly applicable to our problem. In the field of watermarking, a fixed image or mark is embedded in an image often with the goal of identifying fraudulent copies of a video, image or document. Existing work emphasizes almost exclusively the digital domain and does not account for the effect of illumination in the image formation process in real world scenes. In the digital domain, neglecting the physics of illumination is quite reasonable; however, for camera-display messaging, illumination plays a central role.
@cite_41 @cite_21 @cite_1 @cite_40 @cite_27 @cite_10
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This paper presents a new approach for the secure and robust copyright protection of digital images. A system for generating digital watermarks and for trading watermarked images is described. The system is based on a new watermarking technique, which is robust against image transformation techniques such as compression, rotation, translation, scaling and cropping. It uses modulation of the magnitude components in Fourier space to embed a watermark and an accompanying template and, during watermark extraction, reads a template in the log polar transform of the frequency domain. The template is used for analyzing scaling and rotation suffered by the watermarked stego-image. The detection of the watermarks is also possible without any need for the original cover-image. In addition, the system applies asymmetric cryptographic protocols for different purposes, namely embedding detecting the watermark and transferring watermarked data. The public key technique is applied for the construction of a one-way watermark embedding and the verification function to identify and prove the uniqueness of the watermark. Legal dispute resolution is supported for the multiple watermarking of a digital image without revealing the confidential keying information. Abstract In this paper, we propose a practical application for copyright protection of images with watermarking. The EXIF metadata of images and error-control codes are integrated into our algorithm and corresponding applications. Application for robust watermarking is one of the major branches in digital rights management (DRM) systems and digital forensics. Here, we focus on the copyright protection for images taken by ordinary cameras. By use of robust watermarking, it generally alters selected coefficients of the contents to accomplish the embedding process. Should the received image be in question, the watermark embedded beforehand can be extracted to indicate the copyright owner of such an image. We consider not only the image contents itself, but we also employ the EXIF metadata, which serves as the role of watermark, to be integrated into our scheme to make copyright protection possible. Moreover, for enhancing the performance for copyright protection, channel coding is employed and better protection capability can be expected. Taking the manufacturer, camera model, date and time stamp, and other important information in the EXIF metadata into account, conventional watermarking techniques can be applied to ordinary pictures taken by ourselves, and watermarked images with good quality can be produced. Even when the marked image has been intentionally modified, the original EXIF with selected information can mostly be recovered from the channel decoding process. Simulation results present the effectiveness of such an implementation. Existing techniques of watermarking make use of transform domain to have better robustness towards attacks. Here, we propose a novel learning based auto-encoder Convolutional Neural Network (CNN) for non-blind watermarking which outperforms the existing frequency domain techniques in terms of imperceptibility and robustness adding new dimension of usage of CNNs towards security. As these CNNs efficiently learn the features and represent the input at the output, they find applications in all the fields of science. Code book images of different size are generated using the proposed architecture and subjected to different attacks. Results of the proposed method are compared with state of the art methods at different noises and attacks such as Gaussian, speckle, compression effects, cropping, filtering, etc. The proposed scheme is validated against various possible attacks and its out-performance with state of the art methods is presented. Further, transfer learning capabilities of auto-encoder CNN for efficient way of learning new code book is presented. The inability of intruder towards retrieval of data without the knowledge of architecture and keys employed is also discussed. Illumination changes are a typical problem for many outdoor long-term applications such as visual place recognition. Keypoints may fail to match between images taken at the same location but different times of the day. Although recently some methods are presented for creating shadow-free image representations, all of them have the limitation in terms of dealing with night images and non-Planckian source of lighting. In this paper we present a new method for creating illumination invariant image representation using a combination of two existing methods based on natural image statistics that address the issue of illumination invariance. Unlike previous attempts at solving the problem of illumination invariant representation, the proposed method does not assume the ideal narrow-band color camera nor a calibration step for each environment. We evaluate our method on real datasets to establish its accuracy and efficiency. Experimental results show that our method outperforms competing methods for illumination invariant image representation.