An example symbol-based watermark detection method disclosed herein includes, in response to a comparison of a first count of occurrences of a first potential symbol value corresponding to a first symbol within a watermark and a second count of occurrences of a second potential symbol value corresponding to the first symbol, (i) determining a first accumulated signal to noise ratio value corresponding to the occurrences of the first potential symbol value, (ii) determining a second accumulated signal to noise ratio value corresponding to the occurrences of the second potential symbol value, and (iii) selecting one of the first or the second potential symbol value having a greatest accumulated signal to noise ratio value as a likely symbol value for the first symbol. The example method also includes concatenating the likely symbol value with other likely symbol values corresponding to other symbols of the watermark to detect the watermark.

Imagery captured by an autonomous robot is analyzed to discern digital watermark patterns. In some embodiments, identical but geometrically-inconsistent digital watermark patterns are discerned in an image frame, to aid in distinguishing multiple depicted instances of a particular item. In other embodiments, actions of the robot are controlled or altered in accordance with image processing performed by the robot on a digital watermark pattern. The technology is particularly described in the context of retail stores in which the watermark patterns are encoded, e.g., on product packaging, shelving, and shelf labels. A great variety of other features and arrangements are also detailed.

This disclosure relates to advanced image signal processing technology including encoded signals and digital watermarking. The technology may be applied to retail packages and other printed objects, e.g., such as hang tags, labels and receipts.

This disclosure relates to advanced image signal processing technology including encoded signals and digital watermarking. One implementation is directed a printed object comprising: a substrate comprising a first area; a first colored ink or design printed within the first area, the first colored ink or design comprising a spectral reflectivity of less than or equal to 20% at or around 660 nm; a colored ink mixture printed over the first colored ink or design at a first plurality of spatial locations within the first area, the colored ink mixture printed such that the first area comprises a second plurality of spatial locations without the colored ink mixture, the colored ink mixture comprising opaque white ink and a first colorant, wherein the color ink mixture comprises a spectral reflectivity greater than the first colored ink or design at or around 660 nm, and wherein colored ink mixture comprises a spectral reflectivity less than the first colored ink or design in the range of 495 nm-570 nm; in which the first plurality of spatial locations is arranged in a pattern conveying an encoded signal, and in which the first colored ink or design and the colored ink mixture comprise a spectral reflectivity difference at or around 660 nm in a difference range of 8%-30%. Of course, other objects, methods, packages, labels, containers, systems and apparatus are described in this patent document.

A system, method and computer program product for protecting a deep neural network image classifier against receiving perturbed images. A plurality of watermark bits are embedded into an original digital image intended for the deep neural network image classifier. The watermarked image is transmitted through a potentially adversarial environment. A potentially perturbed image intended for the deep neural network image classifier is received from the potentially adversarial environment. The potentially perturbed image is determined to be an adversely modified or benign image by determining whether the potentially perturbed image includes a plurality of embedded bits matching the plurality of watermark bits embedded into the original digital image. The potentially perturbed image is prevented from being provided to the deep neural network image classifier in response to determining that the potentially perturbed image is the adversely modified image.

The present disclosure relates generally to data hiding for retail product packaging and other printed objects such as substrates. One embodiment embeds an information signal in a spot color for printing on various substrates. The spot color is screened, and overprinted with process color tint. The tint is modulated prior to overprinting with optimized signal tweaks. The optimization can include consideration of a detector spectral dependency (e.g., red and/or green illumination). Many other embodiments and combinations are described in the subject patent document.

A system, device and method for fingerprint authentication using a watermarked digital image is provided. A device includes a display device including a touch screen configured to detect fingerprints; and, a controller. The controller: generates, at the display device, an image that includes, in one or more given areas, image-embedded fingerprint information; detects, at one or more portions of the touch screen respectively corresponding to the one or more given areas, user-fingerprint information representing a fingerprint; implements a comparison between the user-fingerprint information and the image-embedded fingerprint information; and when the comparison between the user-fingerprint information and the image-embedded fingerprint information is successful, implement an access process.

Authenticity of a sticker (e.g., a mark-down sticker on a retail item), or integrity of a closure (e.g., on a delivery bag or package), is confirmed by reference to spatial information. In some embodiments a fingerprint is formed from parameters describing spatial placement of a sticker or pattern on a substrate. In some embodiments a digital watermark pattern provides a spatial frame of reference within which one or more other features can be located. A great many other features and arrangements are also detailed.

In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed views—further minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. Logos may be identified and used—or ignored—in product identification. A great variety of other features and arrangements are also detailed.

Methods, apparatus, systems and articles of manufacture are disclosed to perform symbol-based watermark detection. An example method includes, in response to determining that a first rate of occurrence of a first potential symbol equals a second rate of occurrence of a second potential symbol, a first accumulated signal to noise ratio value corresponding to occurrences of the first potential symbol value is determined. A second accumulated signal to noise ratio value corresponding to occurrences of the second potential symbol value is determined. One of the first potential symbol value or the second potential symbol value having a greatest accumulated signal to noise ratio value as the likely symbol value is selected. The likely symbol value is concatenated with other likely symbol values corresponding to other symbols of the watermark to detect the watermark.