Fujitsu Develops Technology to Block Facial Authentication Fraud
Fujitsu Laboratories Ltd. announced the development of a facial recognition technology that uses conventional cameras to successfully identify efforts to spoof authentication systems. This includes impersonation attempts in which a person presents a printed photograph or an image from the internet to a camera. Fujitsu has developed a forgery feature extraction technology that detects the subtle differences between an authentic image and a forgery, as well as a forgery judgment technology that accounts for variations in appearance due to the capture environment.
Fujitsu’s new technology ultimately makes it possible to prevent impersonation with forgeries using only face images taken at the time of authentication, enhancing security without sacrificing the convenience of face authentication and contributing to the DX (digital transformation) of operations with improved personal authentication technologies. Fujitsu has developed a technology that can detect the impersonation of others through photographs, etc. from face images taken with a general-purpose camera.
Forgery Feature Extraction Technology Based on Characteristics in Photographic Appearance Unique to Counterfeit
Various features characteristic of a forgery remains in images obtained by presenting the forgery to the camera, such as reflections on the terminal screen of a smartphone, and distortion of the shape of the face caused by taking a planar forgery. Fujitsu has developed a forgery feature extraction technique to express the difference between the forgery’s characteristic features and the real face as determinable values
First, the face image captured by the camera is separated into various elements that exhibit the characteristic features of forgery, such as reflection elements and shape elements. Next, image processing technology is used to digitize the characteristic features of forgery for each of the separated elements, and the characteristics of each element are combined to generate a characteristic for judgment. This makes it possible to identify counterfeits without information based on user operations.
Technology for Judging Forgery in Response to Variation in Image Quality due to Capture Environment
In the past, in order to respond to variations in image appearance caused by the capture environment, a single determination model was generated by training a system with face images containing various variations using machine learning. However, the wide range of variations in the way images are taken, depending on the type of forgery, such as a smartphone screen or ID card, complicates the boundary between the real face and the forgery, making it difficult to identify the forgery even with the latest Deep Learning techniques. Therefore, Fujitsu has developed a technology that can correctly identify counterfeits by generating determination models that reduce the influence of variations by learning the categories of face images that have similar variations, such as face images taken at the office or face images taken by a window.
The development technology steps are divided into a training phase and a judgment phase. In the training phase, face images acquired in various environments are classified into categories such as window, backlight, and normally based on the capture environment, such as the intensity of light and the direction of light. Next, a judgment model is made for determining whether the target is a real face or a counterfeit with machine learning, using the decision features generated by the forgery feature extraction technology for each category
In the judgment phase, in order to estimate which of the categories defined in the training phase the input image capture environment is close to, the similarity between the input image and each category is calculated dynamically. Next, in order to emphasize the result of the determination model of the category in which the input image and the environment are close to each other, a value obtained by multiplying the score indicating the authenticity output from each determination model by the weight based on the similarity with each category is used to determine whether or not the object is a fake (Figure 3 (b)).
By using these technologies, it becomes possible to identify counterfeits using only the information of face images taken by a general-purpose camera and to realize relatively convenient and inexpensive spoofing detection. Fujitsu aims to further improve the accuracy of its forgery detection technology with the aim of putting it into practical use by the end of the fiscal 2020 in March 2021.
(This content is surmised from a press release)