Detecting Double JPEG Compressed Color Images via an Improved Approach

Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.

CM-Droid: Secure Container for Android Password Misuse Vulnerability

Android applications are associated with a large amount of sensitive data,therefore application developers use encryption algorithms to provide user data encryption,authentication and data integrity protection.However,application developers do not have the knowledge of cryptography,thus the cryptographic algorithm may not be used correctly.As a result,security vulnerabilities are generated.Based on the previous studies,this paper summarizes the characteristics of password misuse vulnerability of Android application software,establishes an evaluation model to rate the security level of the risk of password misuse vulnerability and develops a repair strategy for password misuse vulnerability.And on this basis,this paper designs and implements a secure container for Android application software password misuse vulnerability:CM-Droid.

An Efficient Detection Approach of Content Aware Image Resizing

Content aware image resizing(CAIR)is an excellent technology used widely for image retarget.It can also be used to tamper with images and bring the trust crisis of image content to the public.Once an image is processed by CAIR,the correlation of local neighborhood pixels will be destructive.Although local binary patterns(LBP)can effectively describe the local texture,it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise.Therefore,to deal with the detection of CAIR,a novel forensic method based on improved local ternary patterns(ILTP)feature and gradient energy feature(GEF)is proposed in this paper.Firstly,the adaptive threshold of the original local ternary patterns(LTP)operator is improved,and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR.Secondly,the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection.Then,the ILTP features and the gradient energy features are concatenated into the combined features,and the combined features are used to train classifier.Finally support vector machine(SVM)is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not.The candidate images are extracted from uncompressed color image database(UCID),then the training and testing sets are created.The experimental results with many test images show that the proposed method can detect CAIR tampering effectively,and that its performance is improved compared with other methods.It can achieve a better performance than the state-of-the-art approaches.

Protection of Li metal anode by surface-coating of PVDF thin film to enhance the cycling performance of Li batteries

Lithium metal, the ideal anode material for next-generation high-energy batteries, suffers from the severe safety problem of Li dendrites. Herein, we report a simple approach to effectively maintain the morphology of Li-metal anode and enhance the cycling performance of Li batteries by surface coating of a porous polyvinylidene fluoride (PVDF) thin film. In symmetrical cells testing, the cells with the Li@PVDF electrode display stable cycling performance more than 1300 h (650 cycles) at the current density of 0.5 mA/cm^2 with a stripping/plating capacity of 0.5 mAh/cm^2. The results with full cells employing Li@PVDF anode and LiFePO_4 cathode show a good cycling ability with a capacity retention of 80.0% after 500 cycles at 4 C and an excellent rate capability with a high capacity of 78.4 mAh/g even at a high rate of 10 C.