Adobe, Inc.

Lilly Kumari

Vishwa Vinay

Ritwik Sinha

Sunny Dhamnani

Niyati Himanshu Chhaya

David Meier Weinstein

Margarita Savova

Vineet Vinayak Pasupulety

Rutuj Jugade

Nitin Rathor

Methods, systems, and non-transitory computer readable storage media are disclosed for detecting robotic activity while monitoring Internet traffic across a plurality of domains. For example, the disclosed system identifies network session data for each domain of a plurality of domains, the network session data including network sessions comprising features that indicate human activity. In one or more embodiments, the disclosed system generates a classifier to output a probability that a network session at a domain includes human activity. In one or more embodiments, the disclosed system also generates a classifier to output a probability that a network session includes good robotic activity. Additionally, the disclosed system generates a domain-agnostic machine-learning model by combining models from a plurality of domains with network sessions including human activity.

Detecting robotic internet activity across domains utilizing one-class and domain adaptation machine-learning models

A session identification system classifies network sessions with a network application as either human-generated or generated by a non-human, such as by a bot. In an embodiment, the session identification system receives a set of unlabeled network sessions, and determines a label for a single class of the unlabeled network sessions. Based on the one-class labeling information, the session identification system determines multiple subsets of the unlabeled network sessions. Multiple classifiers included in the session identification system generate probabilities describing each of the unlabeled network sessions. The session identification system classifies each of the unlabeled network sessions based on a combination of the generated probabilities.

Classification of website sessions using one-class labeling techniques

Makeup identification using deep learning in a digital medium environment is described. Initially, a user input is received to provide a digital image depicting a face which has a desired makeup characteristic. A discriminative neural network is trained to identify and describe makeup characteristics of the input digital image based on data describing differences in visual characteristics between pairs of images, which include a first image depicting a face with makeup applied and a second image depicting a face without makeup applied. The makeup characteristics identified by the discriminative neural network are displayed for selection to search for similar digital images that have the selected makeup characteristic. Once retrieved, the similar digital images can be displayed along with the input digital image having the desired makeup characteristic.

Makeup identification using deep learning

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Patna, India

Lilly Kumari