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2012_bkup

2012-13

2013_bkup

access_log_prediction

Predict access result based on past result

adversarial-attacks-pytorch

A pytorch implementations of Adversarial attacks and utils

adversarial_audio

adversarial_cfiar10

CIFAR 10 ADVERSARIAL SAMPLES

adversarial_examples

对抗样本

adversarial_mnist

advertorch

A Toolbox for Adversarial Robustness Research

android_live_camera_gag

Camera Meme Setter

android_webview_app_for_youtube_video_fullscreen_play_without_flashplayer

Android webview app for page containing youtube video fullscreen play without flashplayer

androidendlessrunnergame

Android Endless Runner Game

androidimagequiz

Android Image based quiz app

androidslotmachine

AndroidSlotMachine

attack-and-defense-methods

A curated list of papers of adversarial examples and defense methods.

authentication_by_stylometry_nlp

This Projects aim to evaluate the efficiency of Stylometry approach as an authentication method. Stylometric analysis research has been done for author identification and there is significant progress to recognize an author based on their written texts. In this project, we tried to detect differences between writing styles on the same topic provided by a set of users and we test that these differences are enough to use for an authentication system or not.

basketball_unity3d

bayou-distributed-system

bayou distributed system implementation in C for meeting room scheduler

black-box-audio

Targeted Adversarial Examples for Black Box Audio Systems

bpda

BPDA adversarial attack on PGD defense

candycrash

Android candy crash game where candy comes from gallery image

central_transformation

The census transform (CT) is an image operator that associates to each pixel of a grayscale image a binary string, encoding whether the pixel has smaller intensity than each of its neighbours, one for each bit. It is a non-parametric transform that depends only on relative ordering of intensities, and not on the actual values of intensity, making it invariant with respect to monotonic variations of illumination, and it behaves well in presence of multimodal distributions of intensity, e.g. along object boundaries.[1] It has applications in computer vision, and it is commonly used in visual correspondence problems such as optical flow calculation and disparity estimation.[2] The census transform is related to the rank transform, that associates to each pixel the number of neighbouring pixels with higher intensity than the pixel itself, and was introduced in the same paper.[3] Algorithm The most common version of the census transform uses a 3x3 window, comparing each pixel p {\displaystyle p} p with all its 8-connected neighbours with a function ξ {\displaystyle \xi } \xi defined as ξ ( p , p ′ ) = { 0 if p > p ′ 1 if p ≤ p ′ . {\displaystyle \xi (p,p')={\begin{cases}0&{\text{if }}p>p'\\1&{\text{if }}p\leq p'\\\end{cases}}.} {\displaystyle \xi (p,p')={\begin{cases}0&{\text{if }}p>p'\\1&{\text{if }}p\leq p'\\\end{cases}}.} The results of these comparisons are concatenated and the value of the transform is an 8-bit value, that can be easily encoded in a byte. 124 74 32 124 64 18 157 116 84 ⟶ 1 1 0 1 x 0 1 1 1 ⟶ 11010111 {\displaystyle {\begin{array}{|c|c||c|}\hline 124&74&32\\\hline 124&64&18\\\hline 157&116&84\\\hline \end{array}}\longrightarrow {\begin{array}{|c|c|c|}\hline 1&1&0\\\hline 1&x&0\\\hline 1&1&1\\\hline \end{array}}\longrightarrow 11010111} {\displaystyle {\begin{array}{|c|c||c|}\hline 124&74&32\\\hline 124&64&18\\\hline 157&116&84\\\hline \end{array}}\longrightarrow {\begin{array}{|c|c|c|}\hline 1&1&0\\\hline 1&x&0\\\hline 1&1&1\\\hline \end{array}}\longrightarrow 11010111} Similarity between images is determined by comparing the values of the census transform for corresponding pixels, using the Hamming distance.[3] Several variations of the algorithm exist, using different size of the window, order of the neighbours in the pattern (row-wise, clockwise, counterclockwise), comparison operator (greater, greater or equal, lesser, lesser or equal).[4] An extension of the algorithm uses a three-way comparison that allows to represent similar pixels, whose intensity difference is smaller than a tolerance parameter ϵ {\displaystyle \epsilon } \epsilon , defined as[5] ξ ( p , p ′ ) = { 0 if p − p ′ > ϵ 1 if | p − p ′ | ≤ ϵ 2 if p ′ − p > ϵ {\displaystyle \xi (p,p')={\begin{cases}0&{\text{if }}p-p'>\epsilon \\1&{\text{if }}|p-p'|\leq \epsilon \\2&{\text{if }}p'-p>\epsilon \end{cases}}} {\displaystyle \xi (p,p')={\begin{cases}0&{\text{if }}p-p'>\epsilon \\1&{\text{if }}|p-p'|\leq \epsilon \\2&{\text{if }}p'-p>\epsilon \end{cases}}} whose result can be encoded with two bits for each neighbour, thus doubling the size of the pattern for each pixel. 124 74 32 124 64 18 157 116 84 ⟶ 2 1 0 2 x 0 2 2 2 ⟶ 21020222 {\displaystyle {\begin{array}{|c|c||c|}\hline 124&74&32\\\hline 124&64&18\\\hline 157&116&84\\\hline \end{array}}\longrightarrow {\begin{array}{|c|c|c|}\hline 2&1&0\\\hline 2&x&0\\\hline 2&2&2\\\hline \end{array}}\longrightarrow 21020222} {\displaystyle {\begin{array}{|c|c||c|}\hline 124&74&32\\\hline 124&64&18\\\hline 157&116&84\\\hline \end{array}}\longrightarrow {\begin{array}{|c|c|c|}\hline 2&1&0\\\hline 2&x&0\\\hline 2&2&2\\\hline \end{array}}\longrightarrow 21020222} See also

cidmp

CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space

clustering_fgsm_vs_mnist

comp4432_exercises

Exercises for comp4432

conference_deadlines

Top computer science conference deadlines for AI, Computer vision, HCI, Automation and security

control_pc_via_lan_csharp

Local PC control over lan via C#

cs487_applied_machine_learning_python

Implement basic machine learning tasks, such as classification, regression, clustering, etc.

cut_the_rope_type_game

cut the rope type game for android box2d

datadrivenmedicalimaging