Sap Se

Samsung Electronics Co., Ltd.

Yuri Arshavski

Lev Sigal

Anton Ioffe

Juliy Broyda

Suchitra Sundararaman

Eyal Toledano

Jesper B Lind

Orna Bregman-Amitai

Ran Bittmann

Amir Shaham

Eduard Oks

Jesper Lind

Michael Kemelmakher

Rami Gil

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes training at least one machine learning model to determine features that can be used to determine whether an image is an authentic image of a document or an automatically generated document image, using a training set of authentic images and a training set of automatically generated document images. A request to classify an image as either an authentic image of a document or an automatically generated document image is received. The machine learning model(s) are used to classify the image as either an authentic image of a document or an automatically generated document image, based on features included in the image that are identified by the machine learning model(s). A classification of the image is provided. The machine learning model(s) are updated based on the image and the classification of the image.

Anomaly and fraud detection with fake event detection using machine learning

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving a request to authenticate a document image. The image is preprocessed to prepare the image for line orientation analysis. The preprocessed image is analyzed to determine lines in the preprocessed image. The determined lines are automatically analyzed by performing line orientation test(s) on the determined lines to generate line orientation test result(s) for the preprocessed image. The line orientation test result(s) are evaluated to determine whether the image is authentic. In response to determining that at least one line orientation test result matches a predefined condition corresponding to an unauthentic document, a determination is made that the image is not authentic. In response to determining that none of the line orientation test results match any predefined condition corresponding to an unauthentic document, a determination is made that the image is authentic.

Anomaly and fraud detection with fake event detection using line orientation testing

Methods, systems, and computer-readable storage media for receiving a set of frames, each frame being provided as a digital image that depicts a portion of an event and a logo associated with a brand, for each frame in the set of frames, and for each pixel in a frame: determining a weight of the pixel based on a distribution assigned to the frame, providing a quality of the logo depicted in the frame, and calculating a pixel quotient based on the weight and the quality, for each frame in the set of frames: determining a frame quotient at least partially based on a sum of all pixel quotients for the frame, and determining an impact indicator for the logo based on a total size of digital media comprising the set of frames and a sum of frame quotients of the frames in the set of frames.

Quantifying brand visual impact in digital media

A method for generating and viewing on a handheld device a 3-D augmented reality feature containing a rich media message that is linked to a physical object comprises the steps of: a) by a first user: i. Taking a picture of a physical object; ii. selecting an augmented reality theme; iii. attaching the rich media animated object to the image taken, in the desired position and location on the physical object; iv. generating a reach media message from the augmented reality image obtained in step (iii); iv. optionally attaching an additional file to the rich media message; v. Transferring the physical object to a second user; and vi. sending to said second user a message via a communication channel, which contains the augmented reality rich media; b) by the second user (the recipient): vii. viewing the physical object received from the first user, using an AR viewer in the mobile phone camera, thereby to see the augmented reality rich media appearing on said physical object.

Augmented media message

Growing community of inventors

Netanya, Israel

Yuri Arshavski