A method automatically detects digital assets embedded into video frames or images. Each digital asset includes a plurality of embedded graphical representation elements with each element embedding an individual character of the digital asset’s unique identification code. The video frames or images are automatically scanned for the presence of embedded graphical representation elements with each detected element decoded to extract its individual character. The resultant extracted characters are then statistically analyzed in an attempt to reconstruct the digital asset’s unique identification code.

Systems and methods for replacing original media bookmarks of at least a portion of a digital media file with replacement bookmarks is described. A media fingerprint engine detects the location of the original fingerprints associated with the portion of the digital media file and a region analysis algorithm characterizes regions of media file spanning the location of the original bookmarks by data class types. The replacement bookmarks are associated with the data class types and are overwritten or otherwise are substituted for the original bookmarks. The replacement bookmarks then are subjected to a fingerprint matching algorithm that incorporates media timeline and media related metadata.

Methods, systems, and computer programs encoded on a computer storage medium, that relate to extracting digital watermarks from images, irrespective of distortions introduced into these images. Methods can include inputting a first data item into a channel encoder that can generate a first encoded data item that is greater in length than the first data item and that (1) includes the input data item and (2) new data this is redundant of the input data item. Based on the first encoded data item and a first image, an encoder model can generate a first encoded image into which the first encoded data is embedded as a digital watermark. A decoder model can decode the first encoded data item to generate a second data, which can be decoded by the channel decoder to generate data that is predicted to be the first data.

A method of signing prediction-coded video data, comprising: obtaining a coded video sequence including at least one I-frame (I), which contains independently decodable image data, and at least one predicted frame (P1, P2, P3, P4), which contains image data decodable by reference to at least one other frame; generating a fingerprint (H.sub.I) of each I-frame; generating a fingerprint (H.sub.P) of each predicted frame by hashing a combination of data derived from the predicted frame and data derived from an I-frame to which the predicted frame refers directly or indirectly, wherein the fingerprint of the predicted frame is independent of any further predicted frame to which the predicted frame refers directly or indirectly; and providing a signature of the video sequence including the generated fingerprints.

A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects). A great variety of other features and arrangements—some involving designing classifiers so as to combat classifier copying—are also detailed.

Apparatuses, methods, systems, and program products are disclosed for watermark security. An apparatus includes a watermark module configured to generate a digital watermark to be presented as part of a graphical interface based on data presented on the graphical interface. A digital watermark verifies an authenticity of data to be presented in a graphical interface. An apparatus includes a presentation module configured to embed the digital watermark into the graphical interface prior to the data being presented in the graphical interface such that the digital watermark is graphically imperceptible to a user, dynamically update the digital watermark during runtime in response to detecting a change in the at least a portion of the data that is encoded into the digital watermark, and re-embed the digital watermark into the graphical interface in response to the digital watermark being updated.

In a copyright protection assistance system 1, a content delivery device 2 stores embedded region specification information having identification information and a page of comic contents, and definition information of an embedded region in the page, and user information of a logged-in user receiving delivery of the comic contents, embeds, in response to a request for delivery of specific comic contents by a specific user, the user information of the specific user by means of a difficult-to-perceive digital watermark in the embedded region specified by the embedded region specification information corresponding to the identification information of the specific comic contents, delivers the specific comic contents to the specific user, and transmits the identification information of the delivered specific comic contents and the user information of the specific user in association with each other to a copyright management device 3 for storage purpose.

Embodiments are provided that perform cross-application installation of a particular target application program. Such embodiments facilitate cross-application installation of the particular target application program through a computer-executable tool capable of incorporation into any number of distinct application programs, including third-party application programs. Some example embodiments incorporate a computer-executable tool within a first application program installed on a mobile device, execute the first application program on the mobile device and, upon such execution, execute the computer-executable tool to detect system data from the mobile device, determine based at least in part on the system data that an application program set installed to the mobile device does not include a target application program, and in response to the determination use the computer-executable tool to cause rendering of a representation of the target application program within a first user interface associated with the first application program.

Aspects of the subject disclosure may include, for example, a method for training a deep learning model that includes encoding a content item; generating a blended image by combining a background image and the encoded content; decoding the blended image to generate decoded content corresponding to the content item; and defining or specifying a loss function related to the deep learning model. The method also includes determining values of training parameters for the deep learning model to minimize the loss function, thereby obtaining a trained deep learning model. The method also includes an information concealing procedure using the trained deep learning model to conceal user content by encoding the user content and blending the encoded user content with a user-selected image; the information concealing procedure is substantially independent of the user-selected image. Other embodiments are disclosed.

An information handling system improves detection of steganography data embedded in a graphics file by parsing the portable network graphics file to determine a location of a graphics file signature in the graphics file, and determining whether there is data embedded in the graphics file before the graphics signature. The embedded data may then be removed from the graphics file.