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.

A Kahlerness criterion for real (1,1)-classes on projective manifolds through extendibility of singular potentials

Let X be a projective manifold and let{θ}∈H 1,1(X,R)be a nonzero pseudo-effective(transcendental)class,where θ is a smooth closed real(1,1)-form.We prove that if for any one-dimensional complex submanifold C⊂X and φ∈SPsh(C,θ|C)with a single analytic singularity at some point p∈C,there exists a function φ∈Psh(X,θ)such that φ|C=φ and φ is continuous at points of C∖{p},then{θ}is a Kahler class.