Systems and methods for applying and detecting cross dependent marks incorporated into an electronic or digital image to form a watermark. The electronic or digital image may include encoded information for example a machine-readable symbol. The watermarking may include an encoding and insertion sub-process that inserts one or more marks into an image at a first point in time for form a marked image, an extraction sub-process that extracts the marks at a second point in time, and a detection sub-process 108 that determines if any modifications have been made to the marked image. The marked image may be formed by determining a first original descriptor and first original mark within the image, determining a second original descriptor and second original mark within the image, and incorporating the first original mark into the second original descriptor and incorporating the second original mark into the first original descriptor.

In an illustrative embodiment, watermark decoding reliability is increased, for images of watermarked objects captured at close distances, by reducing influence of pixel noise (e.g., shot noise). In the same or different embodiment, watermark decoding reliability is increased, for images of watermarked objects captured from far distances, by reducing image under-sampling. A particular implementation down-samples input imagery twice—a first time by a fixed factor, preparatory to performing an FFT, and a second time by a variable factor, preparatory to submitting the image for decoding, where the variable factor is determined using results from the FFT. A number of other features and arrangements are also detailed.

An image processing method determines a geometric transform of a suspect image by efficiently evaluating a large number of geometric transform candidates in environments with limited processing resources. Processing resources are conserved by using complementary methods for determining a geometric transform of an embedded signal. One method excels at higher geometric distortion, and specifically, distortion caused by greater tilt angle of a camera. Another method excels at lower geometric distortion, for weaker signals. Together, the methods provide a more reliable detector of an embedded data signal in image across a larger range of distortion while making efficient use of limited processing resources in mobile devices.

A watermark image may be generated that includes a first set of encoded pixels each of which is assigned a first transparency value and a second set of encoded pixels each of which is assigned a second transparency value, the second transparency level being different from the first transparency level. The encoded pixels may be distributed among a set of blank pixels such that each encoded pixel neighbors one or more blank pixels in the watermark image, and in particular at least two blank pixels in the watermark image. Herein, each blank pixel may be assigned the second transparency value. The watermark image may be overlaid and blended over a background source image to create an encoded source image. A decoder system may recover encoded information from the encoded source image.

An image synthesis method includes inputting an original image and a watermark image into a synthesis model and obtaining a synthesized image output from the synthesis model. The original image and the watermark image are respectively processed in first and second sub-models of the synthesis model and then combined, and the concatenated result is processed in a third sub-model to generate the synthesized image.

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.

This disclosure relates to counterfeit detection and deterrence using advanced signal processing technology including steganographic embedding and digital watermarking. Digital watermark can be used on consumer products, labels, logos, hang tags, stickers and other objects to provide counterfeit detection mechanisms.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a visually imperceptible or a visually perceptible watermark and outputting a result based on the determination. A watermark decoder receives an input image. The watermark decoder applies a decoder machine learning model to decode a watermarks at different levels of zoom. The water mark decoder determines whether a watermark was decoded to obtain a decoded watermark. The watermark decoder outputs a result based on the determination whether the watermark was decoded through application of the decoder machine learning model to the input image that includes outputting a zoomed output decoded through application of the decoder machine learning model to the input image.

Systems, devices, and methods may use input/output (I/O) apparatus and an optical switching medium to switch, or route, optical data signals. The optical switching medium may include a plurality of optical switching regions. The I/O apparatus may transmit optical data signals to and receive optical data signals from the optical switching medium to provide switching functionality.

An image processing method determines a geometric transform of a suspect image by efficiently evaluating a large number of geometric transform candidates in environments with limited processing resources. Processing resources are conserved by using complementary methods for determining a geometric transform of an embedded signal. One method excels at higher geometric distortion, and specifically, distortion caused by greater tilt angle of a camera. Another method excels at lower geometric distortion, for weaker signals. Together, the methods provide a more reliable detector of an embedded data signal in image across a larger range of distortion while making efficient use of limited processing resources in mobile devices.