A computer-implemented method that provides watermark-based image reconstruction to compensate for lossy encoding schemes. The method can generate a difference image describing the data loss associated with encoding an image using a lossy encoding scheme. The difference image can be encoded as a message and embedded in the encoded image using a watermark and later extracted from the encoded image. The difference image can be added to the encoded image to reconstruct the original image. As an example, an input image encoded using a lossy JPEG compression scheme can be embedded with the lost data and later reconstructed, using the embedded data, to a fidelity level that is identical or substantially similar to the original.

An image code is for information storage, transfer and identification, a method of generating and analyzing the same, and an apparatus for implementing the method, the image code includes a standard image area, at least one image filled in the standard image region, at least one segment of information stream implanted in the at least one image by using digital watermarking technique and 4 location identification graphics arranged in different positions of the standard image region, the location identification graphics are arranged in the 4 vertex angles of the standard image region, including 1 feature location identification graphic and 3 basic location identification graphics; the 3 basic location identification graphics are identical and the feature location identification graphic is different from the basic location identification graphic.

The present disclosure discloses an error modeling method and device for prediction context of reversible image watermarking. A predictor based on omnidirectional context is established; then, the prediction context is self-adaptively error modeled to obtain a self-adaptive error model; and finally, output data from the self-adaptive error model is fed back to the predictor to update and correct the prediction context, so as to correct a prediction value of a current pixel x[i,j]. Since the non-linear correlation between the current pixel and the prediction context thereof, i.e., the non-linear correlation redundancy between pixels can be found by the error modeling of the prediction context of the predictor, the non-linear correlation redundancy between the pixels can be effectively removed. Thus, the embeddable watermarking capacity can be increased.

A method may include obtaining a first byte stream from first document code and a second byte stream from second document code. The first document code has a document type and the second document code has the document type. The method may further include identifying, in the first byte stream, nonvisual noise corresponding to a custom byte code defined in a custom character encoding set. The nonvisual noise is invisible when rendering the first document code. The method may further include replacing, in the first byte stream, the custom byte code with at least one standard byte code defined in a standard character encoding set to obtain modified document code. The second document code uses the standard character encoding set. The method may further include comparing the modified document code with the second document code by comparing the first byte stream with the second byte stream.

The present disclosure provides a method and system for large-capacity image steganography and recovery based on an invertible neural networks. The method is intended to embed one or more hidden images into a single host image, and recover all the hidden images from a stego image. The method designs an image steganography model that supports bidirectional mapping. The model includes cascaded invertible modules containing a host branch and a hidden branch. A hidden image is embedded into a host image through forward mapping to form a stego image, and the host image and the hidden image are separated and recovered from the single stego image through reverse mapping.

An image authentication method is provided. An original image is divided into blocks. An interpolation algorithm is performed on each block so as to obtain a first image. Each pixel in the first image is mapped into an index based on a palette compression technique, so as to generate a second image. Each index is divided into multiple secret values, and a secret sharing algorithm is performed based on the secret values to obtain multiple partial shares. A transparent map is generated according to the partial shares, and a lossless image filed is generated by combining the original image with the transparent map.

The present disclosure discloses an error modeling method and device for prediction context of reversible image watermarking. A predictor based on omnidirectional context is established; then, the prediction context is self-adaptively error modeled to obtain a self-adaptive error model; and finally, output data from the self-adaptive error model is fed back to the predictor to update and correct the prediction context, so as to correct a prediction value of a current pixel x[i,j]. Since the non-linear correlation between the current pixel and the prediction context thereof, i.e., the non-linear correlation redundancy between pixels can be found by the error modeling of the prediction context of the predictor, the non-linear correlation redundancy between the pixels can be effectively removed. Thus, the embeddable watermarking capacity can be increased.

Provided are an information processing device and an information processing method. The information processing device (100) comprises a processing circuit (110) configured to eliminate partial details of at least one part of a three-dimensional model on a condition that a shape semantics of the at least one part is maintained unchanged, so as to generate a modified version of the three-dimensional model. The processing circuit (110) is further configured to control to send the modified version and recovery information to a recipient, wherein the recovery information is used to restore the modified version to the original version of the three-dimensional model.

The present disclosure provides a method and system for large-capacity image steganography and recovery based on an invertible neural networks. The method is intended to embed one or more hidden images into a single host image, and recover all the hidden images from a stego image. The method designs an image steganography model that supports bidirectional mapping. The model includes cascaded invertible modules containing a host branch and a hidden branch. A hidden image is embedded into a host image through forward mapping to form a stego image, and the host image and the hidden image are separated and recovered from the single stego image through reverse mapping.

Provided are an information processing device and an information processing method. The information processing device (100) comprises a processing circuit (110) configured to eliminate partial details of at least one part of a three-dimensional model on a condition that a shape semantics of the at least one part is maintained unchanged, so as to generate a modified version of the three-dimensional model. The processing circuit (110) is further configured to control to send the modified version and recovery information to a recipient, wherein the recovery information is used to restore the modified version to the original version of the three-dimensional model.