nils-herrmann/clean_scirepeval huggingface dataset:
Blind newton sensitivity attack

Until now, the sensitivity attack was considered as a serious threat to the robustness and security of spread-spectrum-based schemes, since it provides a practical method of removing watermarks with minimum attacking distortion. Nevertheless, it had not been used to remove the watermark from other watermarking algorithms, such as those which use side-information. Furthermore, the sensitivity attack has never been used to obtain falsely watermarked contents, also known as forgeries. In this paper a new version of the sensitivity attack based on a general formulation is proposed; this method does not require any knowledge about the detection function nor any other system parameter, but just the binary output of the detector, being suitable for attacking most known watermarking methods. The new approach is validated with experiments.
Modeling Gabor Coefficients via Generalized Gaussian Distributions for Face Recognition

Gabor filters are biologically motivated convolution kernels that have been widely used in the field of computer vision and, specially, in face recognition during the last decade. This paper proposes a statistical model of Gabor coefficients extracted from face images using generalized Gaussian distributions (GGD's). By measuring the Kullback-Leibler distance (KLD) between the pdf of the GGD and the relative frequency of the coefficients, we conclude that GGD's provide an accurate modeling. The underlying statistics allow us to reduce the required amount of data to be stored (i.e. data compression) via Lloyd-Max quantization. Verification experiments on the XM2VTS database show that performance does not drop when, instead of the original data, we use quantized coefficients.
V-DIFT: Vector-Based Dynamic Information Flow Tracking with Application to Locating Cryptographic Keys for Reverse Engineering

Dynamic Information Flow Tracking (DIFT) is a technique for tracking information as it flows through a program's execution. DIFT systems track information by tainting data and propagating the taint marks throughout execution. These systems are designed to have minimal overhead and thus often miss indirect flows. If indirect flows were propagated naively overtainting would result, whereas propagating them effectively causes overhead. We describe the design and evaluation of a system intended for offline analysis, such as reverse engineering, that can track information through indirect flows. Our system, V-DIFT, uses a vector of floating point values for each taint mark. The use of vectors enables us to track a taint's provenance and handle indirect flows, trading off some performance for these abilities. These indirect flows via control and address dependencies are thought to be critical to tracking information flow of cryptographic programs. Therefore we tested V-DIFT's effectiveness by automatically locating keys in simple programs that use a variety of symmetric cryptographic algorithms found in three common libraries. This application does not require that the program run in real time, just that it be much faster than a manual approach. Our V-DIFT implementation tests average 3.6 seconds, and with the right parameters can identify memory locations that contain keys for 24 out of 27 algorithms tested. Our results show that many cryptographic algorithm implementations' address and/or control dependencies must be tracked for DIFT to be effective.
Forensic Detection of Processing Operator Chains: Recovering the History of Filtered JPEG Images

Powerful image editing software is nowadays capable of creating sophisticated and visually compelling fake photographs, thus posing serious issues to the trustworthiness of digital contents as a true representation of reality. Digital image forensics has emerged to help regain some trust in digital images by providing valuable aids in learning the history of an image. Unfortunately, in real scenarios, its application is limited, since multiple processing operators are likely to be applied, which alters the characteristic footprints exploited by current forensic tools. In this paper, we develop a novel forensic technique that is able to detect chains of operators applied to an image. In particular, we study the combination of Joint Photographic Experts Group compression and full-frame linear filtering, and derive an accurate mathematical framework to fully characterize the probabilistic distributions of the discrete cosine transform (DCT) coefficients of the quantized and filtered image. We then exploit such knowledge to define a set of features from the DCT distribution and build an effective classifier able to jointly disclose the quality factor of the applied compression and the filter kernel. Extensive experimental analysis illustrates the efficiency and versatility of the proposed approach, which effectively overcomes the state-of-the-art.
Optimal strategies for spread-spectrum and quantized-projection image data hiding games with BER payoffs

In this paper, we analyze spread-spectrum and quantization projection data hiding methods from a game-theoretic point of view, using the bit error rate (BER) as the payoff, and assuming that the embedder simply follows point-by-point constraints given by a perceptual mask, whereas for the attacker an MSE-like constraint is imposed. The optimal attacking and decoding strategies are obtained by making use of a theorem that in addition states that those strategies constitute an equilibrium of the game. Experimental results supporting our analyses are also shown.
The impact of the cropping attack on scalar STDM data hiding

Scalar spread transform dither modulation (SSTDM), together with its variants, is one of the foremost side-informed data hiding methods. Because of the projection stage of additive spread spectrum (Add-SS), SSTDM achieves the same spreading gain as the former, so that both become robust to additive attacks. We show that the similarities between Add-SS and SSTDM do not extend to the popular cropping attack, since when the removed area increases, performance degrades smoothly for the former but steeply for the latter. This conclusion is arrived at after comparing the analytical expressions derived for both methods. Increasing the size of the projected subspace and using a cubic lattice with repetition coding is shown to be a possible solution to cope with cropping while retaining host interference cancellation.
Detection and information theoretic measures for quantifying the distinguishability between multimedia operator chains

Despite the large number of contributions on forensics in the recent years, there is still a lack of fundamental limits on how easily two signal processing operators (or operator chains) can be distinguished. We take a first step in this direction by proposing the use of some detection and information theoretic measures that are suitable for assessing those fundamental limits. The optimality and the links between those measures are discussed; they are used for quantifying the distinguishability between different operator chains, considering the different prior knowledge that could be available at the forensics analyst. Although this work is mainly a theoretical motivation to the use of those measures in forensic applications, its particularization to three practical scenarios is illustrated.
A Random Matrix Approach to the Forensic Analysis of Upscaled Images

The forensic analysis of resampling traces in upscaled images is addressed via subspace decomposition and random matrix theory principles. In this context, we derive the asymptotic eigenvalue distribution of sample autocorrelation matrices corresponding to genuine and upscaled images. To achieve this, we model genuine images as an autoregressive random field and we characterize upscaled images as a noisy version of a lower dimensional signal. Following the intuition behind Marčenko-Pastur law, we show that for upscaled images, the gap between the eigenvalues corresponding to the low-dimensional signal and the ones from the background noise can be enhanced by extracting a small number of consecutive columns/rows from the matrix of observations. In addition, using bounds provided by the same law for the eigenvalues of the noise space, we propose a detector for exposing traces of resampling. Finally, since an interval of plausible resampling factors can be inferred from the position of the gap, we empirically demonstrate that by using the resulting range as the search space of existing estimators (based on different principles), a better estimation accuracy can be attained with respect to the standalone versions of the latter.
The optimal attack to histogram-based forensic detectors is simple(x)

In the last years a number of counterforensics tools have been proposed. Although most of them are heuristic and designed ad hoc, lately a formal approach to this problem, rooted in transportation theory, has been pursued. This paper follows this path by designing optimal attacks against histogrambased detectors where the detection region is non-convex. The usefulness of our strategy is demonstrated by providing for the first time the optimal solution to the design of attacks against Benford's Law-based detectors, a problem that has deserved large practical interest by the forensic community. The performance of the proposed scheme is compared with that of the best existing counterforensic method against Benford-based detectors, showing the goodness (indeed, the optimality) of our approach.
Optimal counterforensics for histogram-based forensics

There has been a recent interest in counterforensics as an adversarial approach to forensic detectors. Most of the existing counterforensics strategies, although successful, are based on heuristic criteria, and their optimality is not proven. In this paper the optimal modification strategy of a content in order to fool a histogram-based forensics detector is derived. The proposed attack relies on the assumption of a convex cost function; special attention is paid to the Euclidean norm, obtaining the optimal attack in the MSE sense. In order to prove the usefulness of the proposed strategy, we employ it to successfully attack a well-known algorithm for detecting double JPEG compression.
Parameter estimation in spike and slab variational inference for blind image deconvolution

Most current state of the art blind image deconvolution methods model the underlying image (either in the image or filter space) using sparsity promoting priors and perform inference, that is, image, blur, and parameter estimation using variational approximation. In this paper we propose the use of the spike-and-slab prior model in the filter space and a variational posterior approximation more expressive than mean field. The spike-and-slab prior model, which is the "gold-standard" in sparse machine learning, has the ability to selectively shrink irrelevant variables while relevant variables are mildly regularized. This allows to discard irrelevant information while preserving important features for the estimation of the blur which results in more precise and less noisy blur kernel estimates. In this paper we present a variational inference algorithm for estimating the blur in the filter space, which is both more efficient than MCMC and more accurate than the standard mean field variational approximation. The parameters of the prior model are automatically estimated together with the blur. Once the blur is estimated, a non-blind image restoration algorithm is used to obtain the sharp image. We prove the efficacy of our method on both synthetically generated and real images.
Restoration of severely blurred high range images using compound models

We examine the use of compound Gauss Markov random fields (CGMRF) to restore severely blurred high range images. For this deblurring problem, the convergence of the simulated annealing (SA) and iterative conditional mode (ICM) algorithms has not been established. We propose two new iterative restoration algorithms which extend the classical SA and ICM approaches. Their convergence is established and they are tested on real and synthetic images.
Monte Carlo analysis of III–V PIN diodes for tunnel-FETs and Impact Ionization-MOSFETs

III–V Impact-ionization (II) metal-oxide-semiconductor FETs (I-MOSFETs) and tunnel FETs (TFETs) are being explored as promising devices to achieve the best behavior for low power logical circuits. To facilitate the design process of these devices from the physical point of view, a Monte Carlo (MC) model which includes impact ionization events and band-to-band tunneling is presented. Our MC simulator can reproduce the I–V characteristics of experimental ungated In0.53Ga0.47As 100 nm PIN diodes. The tunneling emerges for lower applied voltages than impact ionization events, so that they are found to be appropriate for TFET structures.
Multiframe blind deconvolution of passive millimeter wave images using variational dirichlet blur kernel estimation

Passive Millimeter Wave Images currently used to detect hidden threats suffer from low resolution, blur, and a very low signal-to-noise-ratio. These shortcomings render threat detection, both visual and automatic, very challenging. Furthermore, due to the presence of very severe noise, most of the blind image restoration methods fail to recover the system blurring kernel from a single image. In this paper we propose a robust Bayesian multiframe blind image deconvolution method that approximates the posterior distribution of the blur by a Dirichlet distribution. We show that this approach naturally incorporates the non-negativity and normalization constraints for the blur and cope well with the image noise. The performance of the proposed method is tested on both synthetic and real images.
A Bayesian approach for the estimation and transmission of regularization parameters for reducing blocking artifacts

With block-based compression approaches for both still images and sequences of images annoying blocking artifacts are exhibited, primarily at high compression ratios. They are due to the independent processing (quantization) of the block transformed values of the intensity or the displaced frame difference. We propose the application of the hierarchical Bayesian paradigm to the reconstruction of block discrete cosine transform (BDCT) compressed images and the estimation of the required parameters. We derive expressions for the iterative evaluation of these parameters applying the evidence analysis within the hierarchical Bayesian paradigm. The proposed method allows for the combination of parameters estimated at the coder and decoder. The performance of the proposed algorithms is demonstrated experimentally.
Monte Carlo analysis of thermal effects in GaN HEMTs

By means of a semi-classical Monte Carlo simulator with a consistent self-heating model we have analyzed how the static DC characteristics of GaN HEMTs are modified with respect to the constant room temperature model. The main consequence of the heating of the devices is a decrease of the drain conductance, mainly when the dissipated power is high (high VDS and VGS). Regarding the dynamic SSEC parameters, both Cgs and gm decrease when increasing T, thus reducing the intrinsic cutoff frequency of the transistors.
Cryogenic Performance of Low-Noise InP HEMTs: A Monte Carlo Study

In this paper, we present a study of the cryogenic performance of InP high electron mobility transistors (HEMTs) in the low-noise region by means of Monte Carlo simulations. A decrease of the contact resistances and an increase in the electron velocity in the channel together with enhanced channel electron confinement upon cooling of the device are observed, and considered to be the reason for the excellent low-noise behavior of cryogenic InP HEMTs. These findings are supported by a good agreement between simulated and experimental DC, RF, and noise figure data of a 130-nm gate length InP HEMT at 300 and 77 K. An increase of the transconductance gm and gate-to-source capacitance Cgs is observed when cooling from 300 to 77 K as a consequence of electron velocity increase and improved channel confinement.
Parameter Estimation in Bayesian Reconstruction of Multispectral Images using Super Resolution Techniques

In this paper we present a new super resolution Bayesian method for pansharpening of multispectral images which: a) incorporates prior knowledge on the expected characteristics of the multispectral images, b) uses the sensor characteristics to model the observation process of both panchromatic and multispectral images, and c) performs the estimation of all the unknown parameters in the model. Using real data, the pansharpened multispectral images are compared with the images obtained by other pansharpening methods and their quality is assessed both qualitatively and quantitatively.
Microscopic modeling of nonlinear transport in ballistic nanodevices

By using a semi-classical two-dimensional (2-D) Monte Carlo simulation, simple ballistic devices based on AlInAs/InGaAs channels are analyzed. Our simulations qualitatively reproduce the experimental results in T- and Y-branch junctions as well as in a ballistic rectifier appearing as a result of electron ballistic transport. We show that a quantum description of electron transport is not essential for the physical explanation of these results since phase coherence plays no significant role. On the contrary, its origin can be purely classical: the presence of classical electron transport and space charge inside the structures.
Ultra fast Gunn effect at THz frequencies in HEMTs

When the drain-source bias is sufficiently high the emission spectra of HEMTs show peaks at THz frequencies. In this work, we will show the results of Monte Carlo simulations of 80-nm-gate InAlAs/InGaAs HEMTs where ultra-high frequency (in the THz range) Gunn-like oscillations in the gate-drain region of the devices have been observed. Such high frequency implies that the drift velocity of the high field travelling domain approaches 5times105 m/s (a fivefold increase with respect to the steady-state saturation velocity of electrons). This unexpectedly high velocity comes from the presence of extremely fast ballistic electrons in that region, able to go through the high field domain without overcoming intervalley transfer
InAlAs/InGaAs heteroestructures for THz generation

We present a microscopic analysis of current fluctuations in InAIAs/InGaAs slot diodes (base of HEMT devices). An ensemble Monte Carlo simulation is used for the calculations. We analyze the origin of the strong THz oscillations appearing when the bias surpasses 0.5 V. This effect is apparently caused by the presence of Gunn-like oscillations whose dynamics is controlled by ballistic Gamma-valley electrons in the channel. These carriers are capable to reach extremely high velocities due to (i) the influence of degeneracy effects, which significantly reduces the rate of scattering mechanisms, and (ii) the presence of the recess, which strongly accelerate the electrons, launching them into the drain region. The influence of the effective distance travelled by the domain and the recess length are analyzed in order to improve the control of the frequency and magnitude of the oscillations.
Effects of a High-k Dielectric on the Performance of III–V Ballistic Deflection Transistors

This letter presents a first successful integration of a high-k dielectric, i.e., Al2O3, with III-V semiconductors in ballistic deflection transistors (BDTs). The Al2O3 is deposited using atomic layer deposition, which allows the formation of uniform layers along the walls of etched trenches. The BDT transfer characteristic shows strong dependence on the dielectric permittivity of the material filling the etched trenches. When Al2O3 is deposited in the trenches, the transconductance of the BDT is enhanced and shifted to lower gate bias. Moreover, the ratio between output and leakage currents was also enhanced.
Parameter estimation in Bayesian super-resolution pansharpening using contourlets

In this paper, we consider the problem of parameter estimation on the super resolution and Bayesian methodology for pansharpening using contourlet transform. The used methodology is able to incorporate prior knowledge on the expected characteristics of the multispec-tral images, include information on the unknown parameters in the form of hyperprior distributions and estimate the unknown parameters together with the high resolution multispectral image. The experimental results show that the proposed method not only enhances the spatial resolution of the pansharpened image, but also preserves the spectral information of the original multispectral image.
Monte Carlo simulation of AlGaN/GaN heterostructures

We have developed an ensemble Monte Carlo simulator self-consistently coupled with a 2D Poisson solver for the analysis of AlGaN/GaN and related heterostructures. Special attention has been paid to the implementation of dislocation scattering, allowing to correctly reproduce the measured electron mobility in the GaN channel of the heterostructures. The influence of surface polarization charges at the AlGaN/GaN interface (which leads to an enhanced electron accumulation in the channel) and surface charges at the semiconductor-air interface on the conduction band profile and electron density in the channel has been studied.
Influence of Al mole fraction on the noise performance of GaAs/Al/sub x/Ga/sub 1-x/As HEMT's

By using a Monte Carlo particle simulation, the influence of the Al mole fraction on the intrinsic noise properties of GaAs/GaAs HEMT's is analyzed. P, R, and C noise parameters and the minimum intrinsic noise figure are calculated. The results reveal that the decrease of the energy barrier at the heterojunction as the Al mole fraction is reduced (which leads to a closer approach of the electrons to the gate electrode) makes the noise associated to this electrode increase, consequently degrading the noise performance of the device.
Reconstruction of high-resolution image frames from a sequence of low-resolution and compressed observations

A framework for recovering high-resolution information from a sequence of sub-sampled and compressed observations is presented. Compression schemes that describe a video sequence through a combination of motion vectors and transform coefficients are the focus (e.g. the MPEG and ITU family of standards), and we consider the influence of both the motion vectors and transform coefficients within the reconstruction algorithm. A Bayesian approach is utilized to incorporate the information, and results show a discemable improvement in resolution, as compared to standard interpolation methods.
Monte Carlo Study of an InAlAs/InGaAs Velocity Modulation Transistor

We report a Monte Carlo study of an InP-based InAlAs/InGaAs Velocity Modulation Transistor (VMT) based on the Double-Gate High Electron Mobility Transistor (DG-HEMT), a HEMT with two opposite gates controlling the carrier flow through the conducting channel. In the VMT the source and drain electrodes are connected by two channels with different mobility, and electrons are transferred between the channels by changing the gate voltages in differential mode. As a result, the drain current is modulated while keeping the total carrier density constant, thus in principle avoiding capacitance charging/ discharging delays. The numerical analysis of the carrier density and velocity variations with the gate bias in differential mode demonstrates the actual velocity modulation operation of the proposed VMT transistors.