CNN-based first quantization estimation of double compressed JPEG images

Multiple JPEG compressions leave artifacts in digital images: residual traces that could be exploited in forensics investigations to recover information about the device employed for acquisition or image editing software. In this paper, a novel First Quantization Estimation (FQE) algorithm based on convolutional neural networks (CNNs) is proposed. In particular, a solution based on an ensemble of CNNs was developed in conjunction with specific regularization strategies exploiting assumptions about neighboring element values of the quantization matrix to be inferred. Mostly designed to work in the aligned case, the solution was tested in challenging scenarios involving different input patch sizes, quantization matrices (both standard and custom) and datasets (i.e., RAISE and UCID collections). Comparisons with state-of-the-art solutions confirmed the effectiveness of the presented solution demonstrating for the first time to cover the widest combinations of parameters of double JPEG compressions

IMAGE QUALITY IMPROVEMENT BY ADAPTIVE EXPOSURE CORRECTION TECHNIQUES

The proposed paper concerns the processing of images in digital format and, more specifically, particular techniques that can be advantageously used in digital still cameras for improving the quality of images acquired with a non-optimal exposure. The proposed approach analyses the CCD/CMOS sensor Bayer data or the corresponding color generated image and, after identifying specific features, it adjusts the exposure level according to a ‘camera response ’ like function. 1

Tip-timing measurements of transient vibrations in mistuned bladed disks

Bladed disks are usually characterized by a rich dynamic response during service due to the occurrence of several mode shapes that vibrate at resonance within the operative range. In particular, during start-ups and shutdowns, the variable speed causes a temporary crossing of resonance that cannot be neglected to determine stress envelope and safety margins of the system during its whole mission. In fact, fluid flow induces fluctuating loads with variable frequencies (non-stationary regime) on the blades being responsible of a dynamic response which does not follow the so-called steady-state (stationary) response. This paper proposes a novel post-processing method for Blade Tip-Timing (BTT) measurements for the identification of the resonance parameters of mistuned bladed disks working in non-stationary operative conditions. The method is based on a two degrees of freedom model (2DOF) and focuses on transient resonances in which two mistuned modes with close resonance frequencies are involved in the dynamic response. In such circumstances, the identification method based on the single degree of freedom (1DOF) model usually fails.To verify the effectiveness of the method, numerical and experimental investigations have been performed. First, a mathematical simulator based on a lumped parameter model of a bladed disk system is used to generate the BTT simulated data. Experimental signals are measured using a commercial BTT system through a set of optical probes mounted circumferentially around a rotating dummy blisk. It is shown that the method produces accurate predictions for the numerical simulation, even in the presence of considerable noise levels. Moreover, experimental results confirm a successful implementation of the method on the actual BTT measurements

First Quantization Estimation by a Robust Data Exploitation Strategy of DCT Coefficients

It is well known that the JPEG compression pipeline leaves residual traces in the compressed images that are useful for forensic investigations. Through the analysis of such insights the history of a digital image can be reconstructed by means of First Quantization Estimations (FQE), often employed for the camera model identification (CMI) task. In this paper, a novel FQE technique for JPEG double compressed images is proposed which employs a mixed approach based on Machine Learning and statistical analysis. The proposed method was designed to work in the aligned case (i.e., $8 imes 8$ JPEG grid is not misaligned among the various compressions) and demonstrated to be able to work effectively in different challenging scenarios (small input patches, custom quantization tables) without strong a-priori assumptions, surpassing state-of-the-art solutions. Finally, an in-depth analysis on the impact of image input sizes, dataset image resolutions, custom quantization tables and different Discrete Cosine Transform (DCT) implementations was carried out

Estimating Previous Quantization Factors on Multiple JPEG Compressed Images

The JPEG compression algorithm has proven to be efficient in saving storage and preserving image quality thus becoming extremely popular. On the other hand, the overall process leaves traces into encoded signals which are typically exploited for forensic purposes: for instance, the compression parameters of the acquisition device (or editing software) could be inferred. To this aim, in this paper a novel technique to estimate “previous” JPEG quantization factors on images compressed multiple times, in the aligned case by analyzing statistical traces hidden on Discrete Cosine Transform (DCT) histograms is exploited. Experimental results on double, triple and quadruple compressed images, demonstrate the effectiveness of the proposed technique while unveiling further interesting insights

Exploiting Textons Distributions on Spatial Hierarchy for Scene Classification

This paper proposes a method to recognize scene categories using bags of visual words obtained by hierarchically partitioning into subregion the input images. Specifically, for each subregion the Textons distribution and the extension of the corresponding subregion are taken into account. The bags of visual words computed on the subregions are weighted and used to represent the whole scene. The classification of scenes is carried out by discriminative methods (i.e., SVM, KNN). A similarity measure based on Bhattacharyya coefficient is proposed to establish similarities between images, represented as hierarchy of bags of visual words. Experimental tests, using fifteen different scene categories, show that the proposed approach achieves good performances with respect to the state-of-the-art methods

All-carbon multi-electrode array for real-time in vitro measurements of oxidizable neurotransmitters

We report on the ion beam fabrication of all-carbon multi electrode arrays (MEAs) based on 16 graphitic micro-channels embedded in single-crystal diamond (SCD) substrates. The fabricated SCD-MEAs are systematically employed for the in vitro simultaneous amperometric detection of the secretory activity from populations of chromaffin cells, demonstrating a new sensing approach with respect to standard techniques. The biochemical stability and biocompatibility of the SCD-based device combined with the parallel recording of multi-electrodes array allow: i) a significant time saving in data collection during drug screening and/or pharmacological tests over a large number of cells, ii) the possibility of comparing altered cell functionality among cell populations, and iii) the repeatition of acquisition runs over many cycles with a fully non-toxic and chemically robust bio-sensitive substrate.Comment: 24 pages, 5 figure