scholarly journals Repacked android application detection using image similarity

2020 ◽  
Vol 33 (01) ◽  
pp. 190-199
Author(s):  
M.A. Rahim Khan ◽  
R.C. Tripathi ◽  
Ajit Kumar
Keyword(s):  
Hamming Distance ◽  
Main Idea ◽  
Scanning Speed ◽  
Image Similarity ◽  
Detection Accuracy ◽  
Android Application ◽  
Distance Calculation ◽  
String Distance ◽  
Perceptual Hashing ◽  
Binary Features

The popularity of Android brings many functionalities to its users but it also brings many threats. Repacked Android application is one such threat which is the root of many other threats such as malware, phishing, adware, and economical loss. Earlier many techniques have been proposed for the detection of repacked application but they have their limitations and bottlenecks. In this work, we proposed an image similarity based repacked application detection technique. The proposed work utilized the main idea behind the repacking of application that is “the attacker wants to create fake application looking visually similar to the original". We convert each APK file into a grayscale image and then use perceptual hashing for creating a hash of each image. The string distance algorithms like Hamming distance was used to calculate the distance and searching for the repacked application. The proposed work also used distance calculation on binary features extracted from the app. The proposed work is very powerful in terms of detection accuracy and scanning speed and we achieved 96% accuracy.

scholarly journals Visual Detection for Android Malware using Deep Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 152-156
Keyword(s):  
Deep Learning ◽  
Main Idea ◽  
Mobile Internet ◽  
Android Application ◽  
Analysis Model ◽  
Android Malware ◽  
Proposed Model ◽  
Learning Technique ◽  
Android Applications ◽  
High Level

The most serious threats to the current mobile internet are Android Malware. In this paper, we proposed a static analysis model that does not need to understand the source code of the android applications. The main idea is as most of the malware variants are created using automatic tools. Also, there are special fingerprint features for each malware family. According to decompiling the android APK, we mapped the Opcodes, sensitive API packages, and high-level risky API functions into three channels of an RGB image respectively. Then we used the deep learning technique convolutional neural network to identify Android application as benign or as malware. Finally, the proposed model succeeds to detect the entire 200 android applications (100 benign applications and 100 malware applications) with an accuracy of over 99% as shown in experimental results.

scholarly journals Similarity-based Android malware detection using Hamming distance of static binary features

2020 ◽  
Vol 105 ◽  
pp. 230-247 ◽  
Author(s):  
Rahim Taheri ◽  
Meysam Ghahramani ◽  
Reza Javidan ◽  
Mohammad Shojafar ◽  
Zahra Pooranian ◽  
...  
Keyword(s):  
Hamming Distance ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection ◽  
Binary Features

scholarly journals Unsupervised Scoliosis Diagnosis via a Joint Recognition Method with Multifeature Descriptors and Centroids Extraction

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Liyuan Zhang ◽  
Jiashi Zhao ◽  
Huamin Yang ◽  
Zhengang Jiang ◽  
Qingliang Li
Keyword(s):  
State Of The Art ◽  
Main Idea ◽  
Spinal Curvature ◽  
Initial Contour ◽  
Detection Accuracy ◽  
Recognition Method ◽  
Spine Ct ◽  
Level Set Evolution ◽  
Whole Spine ◽  
The Relationship

To solve the problem of scoliosis recognition without a labeled dataset, an unsupervised method is proposed by combining the cascade gentle AdaBoost (CGAdaBoost) classifier and distance regularized level set evolution (DRLSE). The main idea of the proposed method is to establish the relationship between individual vertebrae and the whole spine with vertebral centroids. Scoliosis recognition can be transferred into automatic vertebral detection and segmentation processes, which can avoid the manual data-labeling processing. In the CGAdaBoost classifier, diversified vertebrae images and multifeature descriptors are considered to generate more discriminative features, thus improving the vertebral detection accuracy. After that, the detected bounding box represents an appropriate initial contour of DRLSE to make the vertebral segmentation more accurate. It is helpful for the elimination of initialization sensitivity and quick convergence of vertebra boundaries. Meanwhile, vertebral centroids are extracted to connect the whole spine, thereby describing the spinal curvature. Different parts of the spine are determined as abnormal or normal in accordance with medical prior knowledge. The experimental results demonstrate that the proposed method cannot only effectively identify scoliosis with unlabeled spine CT images but also have superiority against other state-of-the-art methods.

A Compressed-Domain Image Filtering and Re-Ranking Approach for Multi-Agent Image Retrieval

2015 ◽  
Vol 29 (03) ◽  
pp. 1552001 ◽  
Author(s):  
Jing Zhang ◽  
Zhenwei Li ◽  
Li Zhuo ◽  
Xin Liu ◽  
Ying Yang
Keyword(s):  
Image Retrieval ◽  
Mobile Agent ◽  
Hamming Distance ◽  
Image Filtering ◽  
Compressed Domain ◽  
Compressed Images ◽  
Migration Mechanism ◽  
Multi Agent ◽  
Perceptual Hashing ◽  
Distributed Retrieval

For the limited transmission capacity and compressed images in the network environment, a compressed-domain image filtering and re-ranking approach for multi-agent image retrieval is proposed in this paper. Firstly, the distributed image retrieval platform with multi-agent is constructed by using Aglet development system, the lifecycle and the migration mechanism of agent is designed and planned for multi-agent image retrieval by using the characteristics of mobile agent. Then, considering the redundant image brought by distributed multi-agent retrieval, the duplicate images in distributed retrieval results are filtered based on the perceptual hashing feature extracted in the compressed-domain. Finally, weight-based hamming distance is utilized to re-rank the retrieval results. The experimental results show that the proposed approach can effectively filter the duplicate images in distributed image retrieval results as well as improve the accuracy and speed of compressed-domain image retrieval.

Research on Recommendation System Selection Based on Fuzzy VIKOR with Multiple Distances

2014 ◽  
Vol 598 ◽  
pp. 481-485 ◽  
Author(s):  
Bao Wen Sun ◽  
Ming Li ◽  
Wei Zhang
Keyword(s):  
Hausdorff Distance ◽  
Euclidean Distance ◽  
Recommendation System ◽  
Hamming Distance ◽  
Evaluation Criteria ◽  
Calculation Methods ◽  
Distance Calculation ◽  
Fuzzy Vikor ◽  
Mcdm Method ◽  
System Selection

Nowadays, there are several different kinds of methodology in selecting recommendation systems (CRS), and every method has its own evaluation criteria to pick up the best one. In this paper, a new MCDM method for recommendation system selection based on fuzzy VIKOR with multiple distances is introduced. It selects the best system by calculating values using three different distance calculation methods, which are Hamming distance, Euclidean distance and Hausdorff distance, and voting via Condorcet method. It minimizes the effect of distance and offers a more objective result than other methods and helps enterprises to select the most suitable recommendation system.

Reliable and User Friendly U.S Banknote Recognition Application for Visually Impaired Users on Android Smartphones

2014 ◽  
Vol 5 (3) ◽  
pp. 1-16
Author(s):  
Zhuorui Yang ◽  
James M. Schafer ◽  
Aura Ganz
Keyword(s):  
Visually Impaired ◽  
Detection Accuracy ◽  
Android Application ◽  
Wireless Data ◽  
Lighting Conditions ◽  
Vision Algorithm ◽  
User Friendly ◽  
Data Connection ◽  
Banknote Recognition ◽  
Dark Room

This paper introduces a reliable and user-friendly Android application that helps visually impaired users recognize U.S banknotes. The application relies only on an Android Smartphone and does not require any wireless data connection or back-end infrastructure. The application is proven to be robust and reliable under various environmental conditions which differ in lighting and background conditions. The overall banknote detection accuracy is over 94% in any lighting conditions (even in a dark room) or backgrounds with near real-time banknote detection time of 7 seconds. The main contribution of this paper is the use of a robust and reliable computer vision algorithm on the Android platform combined with a friendly “vision free” user interface.

scholarly journals Image Similarity Estimation Based on Ratio and Distance Calculation between Features

2020 ◽  
Vol 30 (2) ◽  
pp. 147-159
Author(s):  
R. P. Bohush ◽  
S. V. Ablameyko ◽  
E. R. Adamovskiy ◽  
D. Savca
Keyword(s):  
Image Similarity ◽  
Distance Calculation ◽  
Similarity Estimation

scholarly journals Single-Stage Rotation-Decoupled Detector for Oriented Object

2020 ◽  
Vol 12 (19) ◽  
pp. 3262
Author(s):  
Bo Zhong ◽  
Kai Ao
Keyword(s):  
Main Idea ◽  
Ground Truth ◽  
Detection Accuracy ◽  
Bounding Box ◽  
New Strategy ◽  
Dense Distribution ◽  
Matching Strategy ◽  
Bounding Boxes ◽  
Oriented Object ◽  
Oriented Bounding Boxes

Oriented object detection has received extensive attention in recent years, especially for the task of detecting targets in aerial imagery. Traditional detectors locate objects by horizontal bounding boxes (HBBs), which may cause inaccuracies when detecting objects with arbitrary oriented angles, dense distribution and a large aspect ratio. Oriented bounding boxes (OBBs), which add different rotation angles to the horizontal bounding boxes, can better deal with the above problems. New problems arise with the introduction of oriented bounding boxes for rotation detectors, such as an increase in the number of anchors and the sensitivity of the intersection over union (IoU) to changes of angle. To overcome these shortcomings while taking advantage of the oriented bounding boxes, we propose a novel rotation detector which redesigns the matching strategy between oriented anchors and ground truth boxes. The main idea of the new strategy is to decouple the rotating bounding box into a horizontal bounding box during matching, thereby reducing the instability of the angle to the matching process. Extensive experiments on public remote sensing datasets including DOTA, HRSC2016 and UCAS-AOD demonstrate that the proposed approach achieves state-of-the-art detection accuracy with higher efficiency.

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