Systems and methods for insertion of a watermark into images and tampering detection of the watermarked images by a Convolutional Neural Network (CNN) technique. The traditional systems and methods provide for detecting the tampering of the watermarked images by simply identifying a presence of an inserted watermark into an image but none them provide for inserting a random sequence into input image(s) and then detect the tampering by classifying the input image(s) by a neural network. Embodiments of the present disclosure provide for insertion of the watermark into the input image(s) and tampering detection of the watermarked images by training a Convolutional Neural Network (CNN) 201 to classify the images as tampered or non-tampered, extracting random noise, obtaining non-classified watermarked images from the random noise, and obtaining, from the non-classified watermarked images, classified watermarked images and detecting an absence or a presence of the tampering based upon the classified watermarked images.

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.

The present technology relates to image signal processing. One aspect of the present technology involves analyzing reference imagery gathered by a camera system to determine which parts of an image frame offer high probabilities ofrelative to other image partscontaining decodable watermark data. Another aspect of the present technology whittles-down such determined image frame parts based on detected content (e.g., a cereal box) vs expected background within such determined image frame parts.

Systems and methods of detecting an unauthorized data insertion into a stream of data segments extending between electronic modules or between electronic components within a module, wherein a Secret embedded into the data stream is compared to a Replica Secret upon receipt to confirm data transmission integrity.

In one embodiment, a method for 3D digital watermarking for a triangular mesh using one or more key parameters is disclosed including forming a Hamiltonian path of a desired length around a selected vertex in a selected direction of a spiral; marking the selected vertex a dead end if there is a deadlock and continuing the spiral; and applying a watermark by introducing points in a path order on edges of the spiral, wherein information is encoded at a partition of adjacent triangles at one or more of the points.

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 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.

Methods and systems for tracking image senders using client devices are described herein. A computing system may receive an image containing a first watermark vector corresponding to a user account of an image sender. The computing system may convert the image to a frequency domain image that contains the first watermark vector. From the frequency domain image, the computing system may identify the first watermark vector. The computing system may compare the first watermark vector to each of a plurality of stored watermark vectors, each corresponding to a known user account, to determine a probability of a match. The computing system may determine the user account of the sender of the image by determining which of the plurality of stored watermark vectors has a highest probability of a match, and may send, to a workplace administrator platform, an indication of the user account.

Techniques are provided for generation of secure video and tamper detection of the secure video. A methodology implementing the techniques according to an embodiment includes selecting a subset of macroblocks from a video frame to be transmitted and calculating a low frequency metric on each of the selected macroblocks. The method also includes performing a hash calculation on the low frequency metrics to generate a frame signature; encrypting the frame signature (using a private key) to generate an encrypted watermark; and modifying pixels of each of the selected macroblocks to generate the secured video frame, the modifications based on bits of the encrypted watermark that are associated with the selected macroblock. The method further includes authenticating a received video frame by comparing a calculated frame signature to an authenticated frame signature, the authenticated frame signature decrypted (using a public key) from an extracted watermark of the received video frame.

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.