Abstract: Methods and systems for malicious non-human user detection on computing devices are described. The method includes collecting, by a processing device, raw data corresponding to a user action, converting, by the processing device, the raw data to features, wherein the features represent characteristics of a human user or a malicious code acting as if it were the human user, and comparing, by the processing device, at least one of the features against a corresponding portion of a characteristic model to differentiate the human user from the malicious code acting as if it were the human user.

Abstract: This disclosure describes a technique to slow down or block creation of automated attack scripts by configuring a detector to discriminate whether particular attack-like activity is a true attack, or simply a hacker “testing” his or her automated attack script, and then permitting any such test script to continue working (attacking) the site, albeit on a limited basis. In this manner, the hacker receives an indication that his or her automated attack script is already working. Thereafter, when the detector later detects a launch of an actual attack based on or otherwise associated with the automated attack script (previously under test), the attack fails either because the script was not a working script in the first instance, or because information learned about the script is used to adjust the site as necessary to then prepare adequately for a true attack.

Abstract: This disclosure describes a bot detection system that distinguishes bot transactions from human transactions. The system utilizes an anomaly-based filter process to reduce the number of false positives as determined by the system. The filter process includes maintaining a database of anomaly patterns, wherein the patterns are encoded as anomaly pattern strings. As anomalies are detected, they are encoded in the anomaly pattern strings, and the database is updated by maintaining counts on the occurrences of the strings. When a particular pattern string as reflected in the database has a count that exceeds a threshold, the string is determined to be associated with a bot as opposed to a human user.

Abstract: Methods and systems for malicious non-human user detection on computing devices are described. The method includes collecting, by a processing device, raw data corresponding to a user action, converting, by the processing device, the raw data to features, wherein the features represent characteristics of a human user or a malicious code acting as if it were the human user, and comparing, by the processing device, at least one of the features against a corresponding portion of a characteristic model to differentiate the human user from the malicious code acting as if it were the human user.

Abstract: This disclosure describes a technique to determine whether a client computing device accessing an API is masquerading its device type (i.e., pretending to be a device that it is not). To this end, and according to this disclosure, the client performs certain processing requested by the server to reveal its actual processing capabilities and thereby its true device type, whereupon—once the server learns the true nature of the client device —it can take appropriate actions to mitigate or prevent further damage. To this end, during the API transaction the server returns information to the client device that causes the client device to perform certain computations or actions. The resulting activity is captured on the client computing and then transmitted back to the server, which then analyzes the data to inform its decision about the true client device type.

Abstract: Methods and systems for malicious non-human user detection on computing devices are described. The method includes collecting, by a processing device, raw data corresponding to a user action, converting, by the processing device, the raw data to features, wherein the features represent characteristics of a human user or a malicious code acting as if it were the human user, and comparing, by the processing device, at least one of the features against a corresponding portion of a characteristic model to differentiate the human user from the malicious code acting as if it were the human user.

Abstract: A non-transitory computer readable storage medium including instructions that, when executed by a computing system, cause the computing system to perform operations. The operations include collecting, by a processing device, raw data regarding a user action. The operations also include converting, by the processing device, the raw data to characteristic test data (CTD), wherein the CTD represents behavior characteristics of a current user. The operations also include identifying, by the processing device, a characteristic model corresponding to the behavior characteristics represented by the CTD. The operations also include generating, by the processing device, a predictor from a comparison of the CTD against the corresponding characteristic model, wherein the predictor comprises a score indicating a probability that the user action came from an authenticated user.

Abstract: Methods and apparatuses to reduce the number of scaling engines used in a display controller that displays image content associated with a plurality of image sources on a screen are provided. A first multiplexer may receive image content from a plurality of storage devices, and select first image content from one of the plurality of image sources. A first scaling engine may process the selected first image content, wherein a plurality of multiplexers can receives an output of the first scaling engine and the image content from the plurality of storage devices, respectively. Each one of the plurality of multiplexers may output either the image content input to the multiplexer or the output of the first scaling engine depending on which image content was scaled by the first scaling engine. Additionally, a plurality of post-processors may perform additional post-processing on the output of the plurality of multiplexer, respectively.

Abstract: A method including collecting, by a processing device, raw data regarding an input to fill a form field. The method further includes converting, by the processing device, the raw data to test data, wherein the test represents behavioral characteristics of the entry of the input. The method further includes identifying a human characteristic model corresponding to the behavior characteristics of the entry of the input. The method further includes generating a predictor from a comparison of the test data against the corresponding human characteristic model. The predictor includes a score indicating a probability that the input originated from a human user or from a malicious code imitating the human user.

Abstract: A non-transitory computer readable storage medium including instructions that, when executed by a computing system, cause the computing system to perform operations. The operations include collecting, by a processing device, raw data regarding a user action. The operations also include converting, by the processing device, the raw data to characteristic test data (CTD), wherein the CTD represents behavior characteristics of a current user. The operations also include identifying, by the processing device, a characteristic model corresponding to the behavior characteristics represented by the CTD. The operations also include generating, by the processing device, a predictor from a comparison of the CTD against the corresponding characteristic model, wherein the predictor comprises a score indicating a probability that the user action came from an authenticated user.

Abstract: A method including collecting, by a processing device, raw data regarding an input to fill a form field. The method further includes converting, by the processing device, the raw data to test data, wherein the test represents behavioral characteristics of the entry of the input. The method further includes identifying a human characteristic model corresponding to the behavior characteristics of the entry of the input. The method further includes generating a predictor from a comparison of the test data against the corresponding human characteristic model. The predictor includes a score indicating a probability that the input originated from a human user or from a malicious code imitating the human user.

Abstract: Methods and systems may include a compression module having logic to receive a pixel difference signal associated with an image, and conduct a compression of the pixel difference signal based on a value of the pixel difference signal. The logic can also generate a modified pixel difference signal based on the compression, wherein the compression may enable the image to be stored to dynamic random access memory (DRAM) in a compressed state.

Abstract: In some embodiments, a method of processing a video sequence may include receiving an input video sequence having an input video sequence resolution, aligning images from the input video sequence, reducing noise in the aligned images, and producing an output video sequence from the reduced noise images, wherein the output video sequence has the same resolution as the input video sequence resolution. Other embodiments are disclosed and claimed.

Abstract: A method and apparatus for hardware-based anamorphic video scaling. In one embodiment, the method includes the fetch of zero or more new input pixels according to an entry of an input control memory corresponding to a current output pixel. Once fetched, the zero or more new input pixels replace at least one stored input pixel of N, input pixels. Using the updated N, input pixels and an N, coefficient set selected according to an entry of a coefficient memory corresponding to the current output pixel, a pixel computation, such as, for example, an anamorphic scaling computation, is performed. In one embodiment, the anamorphic scaling is performed by subdividing an X×Y pixel frame into X/M M×Y pixel subframes. Other embodiments are described and claimed.

Abstract: Methods and apparatuses to reduce the number of scaling engines used in a display controller that displays image content associated with a plurality of image sources on a screen are provided. A first multiplexer may receive image content from a plurality of storage devices, and select first image content from one of the plurality of image sources. A first scaling engine may process the selected first image content, wherein a plurality of multiplexers can receives an output of the first scaling engine and the image content from the plurality of storage devices, respectively. Each one of the plurality of multiplexers may output either the image content input to the multiplexer or the output of the first scaling engine depending on which image content was scaled by the first scaling engine. Additionally, a plurality of post-processors may perform additional post-processing on the output of the plurality of multiplexer, respectively.

Abstract: Methods and systems may include a transmit apparatus and a receive apparatus. The transmit apparatus can have a first uncompressed video interconnect and an image encoder to generate a compressed bit stream based on an input pixel signal. The image encoder may also send the compressed bit stream to the first uncompressed video interconnect. The receive apparatus may have a second uncompressed video interconnect and an image decoder to receive the compressed bit stream from the second uncompressed video interconnect. The image decoder may also generate an output pixel signal based on the compressed bit stream.

Abstract: A computer system may comprise a graphics controller, which may support a display handler. In one embodiment, the display handler may receive configuration values comprising a quantity value and a blending order. In one embodiment, the display handler may determine the number of universal pixel planes using the quantity value. The display handler may provide a number of universal pixel planes equal to the quantity value and the universal pixel planes may be provided using a reference universal pixel plane. The display handler may render each of the universal pixel planes into a type of pixel plane indicated by the corresponding elements of the blending order.

Abstract: A video scaler is disclosed. A polyphase filter can be used to generate interpolated pixels. The values of pixels adjacent an interpolated pixel are examined to determine variation in values among the adjacent pixels to determine minimum and maximum value variations. The value of the interpolated pixel is limited based on the minimum and maximum value variations. Ringing artifacts can be reduced by limiting the color range of an interpolated pixel.

Abstract: Apparatus, systems and methods for display processing line buffers incorporating pipeline overlap are disclosed. For example, an apparatus is disclosed including processing logic to use pixel processing algorithms to process a pixel value of a first portion of an image, and line buffers coupled to the processing logic. The line buffers to hold at least some pixel values of other portions of the image adjacent to the first portion. Where the pixel values of the other portions of the image held by the line buffers correspond to pixel values of the adjacent portions of the image that are to be convolved by the pixel processing algorithms with the pixel value of the first portion. Other implementations are also disclosed.

Abstract: Apparatus, systems and methods for display processing line buffers incorporating pipeline overlap are disclosed. For example, an apparatus is disclosed including processing logic to use pixel processing algorithms to process a pixel value of a first portion of an image, and line buffers coupled to the processing logic. The line buffers to hold at least some pixel values of other portions of the image adjacent to the first portion. Where the pixel values of the other portions of the image held by the line buffers correspond to pixel values of the adjacent portions of the image that are to be convolved by the pixel processing algorithms with the pixel value of the first portion. Other implementations are also disclosed.