A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper develops a classification method using matrix-variate t-distributions.
Deep learning improves forensic matching of casings.
System identifies power grid location from media recordings.
New model helps identify suspect footwear from crime scene prints.
Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have develo…
This paper reviews deep learning techniques for face recognition and sketch matching.
Bayesian neural networks improve reliability in multimedia forensics.
Recently, image forensics community has paied attention to the research on the design of effective algorithms based on deep learning technology and facts proved that combining the domain knowledge of image forensics and deep learning would achieve more robust and better performance than the traditional schemes. Instead…
We introduce a Bayesian solution for the problem in forensic speaker recognition, where there may be very little background material for estimating score calibration parameters. We work within the Bayesian paradigm of evidence reporting and develop a principled probabilistic treatment of the problem, which results in a…
Deep-learning method estimates bone 3D structure from X-ray images.
Paper proposes a method to locate power grid recordings using ENF sequences.
An important problem in forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing. This procedure, known as geolocation, is conventionally guided by expert knowledge of the biological evidence and therefore tends to be application-specific, labor-i…
New framework classifies generated images into finer categories.
Graph Convolutional Networks show promise for AML in Bitcoin transactions.
An evolutionary algorithm separates mixed DNA profiles in forensic genetics.
Authorship verification (AV) is a research subject in the field of digital text forensics that concerns itself with the question, whether two documents have been written by the same person. During the past two decades, an increasing number of proposed AV approaches can be observed. However, a closer look at the respect…
Network analysis detects insider trading by flagging coordinated trades.
This research adapts superpixels for Shapley value computation in DNA profile classification.
It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than t…
Deep learning detects face swapping with high accuracy and uncertainty.
When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion…
Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traff…
Unsupervised near-duplicate detection has many practical applications ranging from social media analysis and web-scale retrieval, to digital image forensics. It entails running a threshold-limited query on a set of descriptors extracted from the images, with the goal of identifying all possible near-duplicates, while l…
The paper proposes a test to assess rater accuracy while accounting for rater covariates.
A Python tool assesses fairness, accountability, and transparency in AI decisions.
Detects AI-synthesized speech using cepstral and bispectral analysis.
Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take …
New neural network boosts authorship verification on social media.
Owing to the rapid growth of touchscreen mobile terminals and pen-based interfaces, handwriting-based writer identification systems are attracting increasing attention for personal authentication, digital forensics, and other applications. However, most studies on writer identification have not been satisfying because …
Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e.g., fraud detection, climate analysis, or healthcare monitoring. We present an algorithm for detecting anomalous regions in multivariate spatio-temporal time-series, which allows for spotting the interesti…
This paper examines anomalies and frauds in blockchain networks and proposes detection techniques.
Inspection-L detects illicit cryptocurrency transactions using GNNs and self-supervised learning.
Adversarial autoencoder networks detect accounting anomalies in latent space.
In this work we investigate tick-by-tick data provided by the TRTH database for several stocks on three different exchanges (Paris - Euronext, London and Frankfurt - Deutsche Börse) and on a 5-year span. We use a simple algorithm that helps the synchronization of the trades and quotes data sources, providing enhancemen…
Formulates approach for guiding explanation types based on user specifications.
Paper explores vulnerabilities in image authenticity detection methods, especially printing and scanning attacks.
Attack graphs provide compact representations of the attack paths that an attacker can follow to compromise network resources by analysing network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk …
For well over a quarter century, detection systems have been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network event, potential malware sample, suspicious email) is deemed malicious or non-malicious based on its similarity to the learned model at runt…
This paper detects anomalies in cellular network traffic using hybrid methods.
Predicting illegal fishing on Patagonian Shelf using machine learning.
Over the past decades, statisticians and machine-learning researchers have developed literally thousands of new tools for the reduction of high-dimensional data in order to identify the variables most responsible for a particular trait. These tools have applications in a plethora of settings, including data analysis in…
SIGMA model improves graph matching across various applications.
Neural score matching improves high-dimensional causal inference by using neural networks for balancing scores.
Study dynamic matching in heterogeneous networks using ODE model.
Efficiently learns matching rewards in two-sided markets with matrix completion.
The strength of association between a pair of data vectors is represented by a nonnegative real number, called matching weight. For dimensionality reduction, we consider a linear transformation of data vectors, and define a matching error as the weighted sum of squared distances between transformed vectors with respect…
Proposes a dynamic matching algorithm for two-sided online markets.
A classical problem in causal inference is that of matching, where treatment units need to be matched to control units based on covariate information. In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets. This method, called FLAME (Fast Large-scale …