Method for generating an encoded image 1, wherein a camera system provides a camera image 2, wherein the camera image 2 comprises anonymous image sections 3 without personal data and sensitive image sections 4 containing personal data, wherein the camera image 2 is analyzed based on an image recognition algorithm to detect the sensitive image sections 4, wherein an encoded image 1 is generated, wherein in order to generate the encoded image 1 the sensitive image sections 4 of the camera image 2 are anonymized and image contents of the sensitive image sections 4 of the camera image are stored in the encoded image 1 in cryptographically encoded form.

A plurality of byte ranges forms a sample for content output from a player device, and includes at least one double-encrypted byte range. The plurality of byte ranges is stored in a secured memory, and the at least one double-encrypted byte range is partially decrypted to generate at least one decrypted singe-encrypted byte range. The plurality of byte ranges is stored in an unsecured memory using the at least one decrypted single-encrypted byte range in place of the at least one double-encrypted byte range.

A plurality of byte ranges forms a sample for content output from a player device, and includes at least one double-encrypted byte range. The plurality of byte ranges is stored in a secured memory, and the at least one double-encrypted byte range is partially decrypted to generate at least one decrypted singe-encrypted byte range. The plurality of byte ranges is stored in an unsecured memory using the at least one decrypted single-encrypted byte range in place of the at least one double-encrypted byte range.

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.

A computer implemented method for embedding a marker in an image or video content including receiving an input image or frame for embedding, determining a binary message to be encoded within said input image or frame comprising bits sequences having an identical number of bits which is superior or equal to two, said binary message comprising at least a header part comprising at least two consecutive bits sequences which are not identical, detecting a region within said input image or frame such that the color within said region is uniform and that said region presents a chosen length and height, associating each possible bits sequence to a corresponding encoding color determined from the color within said uniform region and an encoding rule such that the respective colors are all different from one another, and generating a marker color table in which each element stores an encoding color associated to a bits sequence of the binary message, such that the color table constitutes a color encoding of the binary message, and embedding said marker in said region by appending directionally pixel blocks comprising at least a chosen number of pixels in an appending direction, the pixels within a given pixel block being each colored with the encoding color of an element of the marker color table, each element of the marker color table being associated with at least one pixel block.

Methods, apparatus and systems for video bitstream generation and parsing are described. One example bitstream decoding method includes parsing a portion of a video bitstream at a video unit level for a first field indicative of whether a slice height is specified for a subpicture partitioning or for a tile partitioning at the video unit level, parsing, due to determining that the first field indicates that the slice height is specified for the subpicture partitioning, N second fields in the portion of the video bitstream to obtain heights for rectangular slices in the video unit, wherein the heights are indicated in multiple of coding tree unit (CTU) heights, and wherein each rectangular slice comprises one or more CTU rows that belong to a same subpicture, wherein N is a positive integer, and decoding, based on the first field and/or the N second fields, the video bitstream to generate a video.

Data is encoded into one or more optically encoded images. The optically encoded images are then inserted as image data into a video sequence - i.e., in video frames. Data are transmitted in-band within the video, via any conceivable video distribution channel or format. The video may be trans-coded as required - because the data are optically encoded, any video processing that even crudely preserves the frame images will preserve the optically encoded data. This scheme of in-band data transfer in video is very robust. A video receiving apparatus receives the video, inspects the image data from video frames in memory, detects optically encoded images in the image data, and decodes the optically encoded images to recover the data. The frames carrying optically encoded images are typically discarded and not rendered to a display. The receiver controls connected equipment, other than a display (e.g., a musical instrument), based on the extracted data.

Data is encoded into one or more optically encoded images. The optically encoded images are then inserted as image data into a video sequence - i.e., in video frames. Data are transmitted in-band within the video, via any conceivable video distribution channel or format. The video may be trans-coded as required - because the data are optically encoded, any video processing that even crudely preserves the frame images will preserve the optically encoded data. This scheme of in-band data transfer in video is very robust. A video receiving apparatus receives the video, inspects the image data from video frames in memory, detects optically encoded images in the image data, and decodes the optically encoded images to recover the data. The frames carrying optically encoded images are typically discarded and not rendered to a display. The receiver controls connected equipment, other than a display (e.g., a musical instrument), based on the extracted data.

Systems, methods, and devices for inserting content into a video frame are disclosed herein. A frame of video data encoded to include a plurality of macroblocks is received. An insertion region of the frame for inserting content is defined, the insertion region spanning a subset of the macroblocks. The frame is augmented with a duplication region configured as a non-displayed region, the duplication region including duplicated macroblocks that duplicate the macroblocks of insertion region. The macroblocks of the insertion region are replaced with replacement macroblocks that encode replacement content.

The present application relates to a steganography method and a steganography apparatus using the same. According to the steganography method of the present application and the steganography apparatus using the same, they can simplify a data refinement process compared to the supervised learning method as AI learns a large amount of data related to steganography encoding and decoding by itself, can generate a high-quality stego video close to the cover video to improve the imperceptibility of hidden information as the generator and discriminator conduct mutual learning, can hide the message in the images and/or sounds according to the learning method to secure a high-capacity cache, and can secure robustness against third-party detection, monitoring, and removal attacks when learning the detection and avoidance methods of physical and technical detection systems (monitoring equipment, wiretapping equipment and security equipment, etc.). In addition, according to the steganography method of the present application, it can simplify a data refinement process compared to the supervised learning method as AI learns a large amount of data related to steganography decoding by itself, and can recover the hidden message close to the original hidden message to improve perceptibility of hidden information as the generator and the discriminator conduct mutual learning.