Secure communication system detects deletions and eavesdropping.
problem Reliable communication over deletion channels with eavesdropping.
method Probabilistic finite-state automata for encoding and decoding.
result Scheme ensures small error probability in decoding and tamper detection.
Adapting deep learning for object detection to detect mixed image tampering.
problem Detecting mixed image tampering without prior knowledge of the method.
method Adapting deep learning for object detection to learn from a large database of various image types, using a Multi-stream Faster RCNN network with fused features from ELA and BAG error maps.
result Improved accuracy in detecting mixed image tampering.
New algorithm rSVDdpd improves robustness and scalability for video surveillance background modeling.
problem Camera tampering and noisy videos make background separation challenging.
method Introduces rSVDdpd, a robust singular value decomposition technique for scalable video surveillance.
result Demonstrates superior performance on benchmark and real-life datasets.
New method detects adversarial images by exploiting their density.
problem Detecting adversarial images that are hard to find with gradient methods.
method Develop a test based on the density of adversarial directions.
result Unprecedented accuracy in detecting adversarial attacks under white-box setting.
Tampering data during training can corrupt neural networks, leading to misclassification.
problem Adversarial attacks on neural networks during training.
method Creating a backdoor in neural networks by modifying training data.
result A single pixel modification to all images of a class in training data can corrupt neural networks, leading to misclassification.
Study explores cyber security threats to EV battery SOC using neural networks.
problem Cyber security threats to electric vehicle battery State of Charge (SOC).
method Back Propagation Neural Network (NN) trained and tested on experimental data.
result BP NN accurately detects cyber-attacks on battery SOC.
HMN detects fake faces using memory networks and visual cues.
problem Detecting fake faces from unseen manipulation types.
method Hierarchical Memory Network (HMN) architecture.
result Superior performance in fake and fraudulent face detection.
Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints in order to fit the outliers. To overcome this problem, data often needs to be pre-processed to remove outliers, though this is difficult for a…
DeFi TrustBoost uses blockchain and AI to assess small business loans.
problem Assessing small business loans from low-wealth households.
method Combines blockchain and Explainable AI to ensure confidentiality, compliance, and security.
result Tamper-proof auditing and on-chain/off-chain data storage for financial organizations.
Paper detects Trojan neural networks with limited or no data.
problem Robustness of DNNs against Trojan attacks in data-scarce scenarios.
method Proposes data-limited and data-free TrojanNet detectors.
result Demonstrates effectiveness of detectors on various datasets.
CheckNet verifies neural network inference on untrusted devices.
problem Ensuring secure and tamper-proof inference on untrusted devices.
method A checksum-based approach for neural network inference verification.
result Excellent attack detection and success bounds on various models.
Defends against strategic data manipulation in machine learning.
problem Adversaries can tamper with datasets to influence learning outcomes.
method Uses multiple learners and strategic activation to counteract attacks.
result Demonstrates effectiveness of a game-theoretic approach to defense.
Blockchain is a distributed database that keeps a chronologically-growing list (chain) of records (blocks) secure from tampering and revision. While computerisation has changed the nature of a ledger from clay tables in the old days to digital records in modern days, blockchain technology is the first true innovation i…
FaceSigns embeds a secret watermark in images to authenticate and detect deepfakes.
problem Realistic image and video manipulation threats, especially deepfakes.
method Semi-fragile watermarking using neural networks, robust to face-swapping but fragile to deepfake manipulations.
result FaceSigns can reliably detect deepfake content with high accuracy.
Paper proposes a SIMO DNN for indoor localization using Wi-Fi fingerprints.
problem Indoor localization with high accuracy and efficiency.
method Single-input and multi-output deep neural network architecture.
result SIMO-DNN scheme outperforms existing methods in floor detection and location accuracy.
New method for robust PCA with exponential family distributions.
problem Recovering low-rank structure from data matrices with outliers.
method Alternating Direction Method of Multipliers for eextRPCA. result Demonstrated effectiveness in steel sheet defect detection and crime activity monitoring.
Post-Quantum Secure Federated DeFi for Inclusive Banking
problem Financial systems and DeFi ecosystems are vulnerable to quantum computing threats.
method Post-Quantum Secure Federated DeFi framework using lattice-based FHE.
result End-to-end homomorphic computation enables inter-bank collaboration.
It is usual to consider data protection and learnability as conflicting objectives. This is not always the case: we show how to jointly control inference --- seen as the attack --- and learnability by a noise-free process that mixes training examples, the Crossover Process (cp). One key point is that the cp~is typicall…
Paper proposes real-time risk metrics for stablecoin protocols.
problem Lack of risk management frameworks for stablecoins.
method Developed two risk metrics: capitalization and liquidity.
result Demonstrated practical benefits of real-time on-chain data.
Study finds Aave token network has core-periphery structure, with high decentralization predicting better returns.
problem Understanding the actual decentralization in DeFi token transactions on the Ethereum blockchain.
method Applied social network analysis to measure decentralization in Aave token transactions.
result A more decentralized Aave token network predicts higher returns and lower volatility.
FedCoin uses blockchain to fairly distribute incentives in federated learning.
problem Fairly incentivizing data owners in federated learning with privacy concerns.
method FedCoin uses a blockchain-based peer-to-peer payment system with a proof of Shapley (PoSap) protocol to calculate and distribute Shapley Values.
result FedCoin accurately computes Shapley Values and promotes high-quality data contributions.
FOCUS addresses label quality disparity in FL for healthcare applications.
problem Label quality disparity in federated learning for healthcare applications.
method FOCUS maintains a small set of benchmark samples and computes the mutual cross-entropy between local and benchmark datasets to quantify label credibility. It then adjusts client weights based on credibility values.
result FOCUS effectively reduces the impact of noisy labels from clients, improving model performance.
Proposes using mode connectivity to improve adversarial robustness of neural networks.
problem Improving adversarial robustness of deep neural networks.
method Employing mode connectivity in loss landscapes to study adversarial robustness and propose methods for improvement.
result Path connection learned using limited bonafide data can effectively mitigate adversarial effects while maintaining original accuracy.
New algorithm finds corrupted vertices in graphs with few queries.
problem Adversarial tampering of graph edges and vertices.
method Active learning algorithm with polynomial query complexity.
result Efficiently recovers corrupted vertices with small query complexity.
Study blockchain's impact on primary financial market challenges.
problem Challenges of blockchain in securities issuance and trading.
method Hybrid method combining interviews and surveys.
result Complex due diligence, mismatch, and difficult monitoring are significant challenges.
New findings show strong barriers against computational robustness in adversarial learning.
problem Computational robustness of classifiers against adversarial attacks.
method Information-theoretic and computational proofs for evasion and poisoning attacks.
result Polynomial-time attacks can find adversarial examples of O(√n) Hamming distance for product distributions.
Study shows adversarial attacks are more likely when test instances are concentrated, leading to vulnerabilities in robust classifiers.
problem Adversarial attacks on robust classifiers when test instances are concentrated.
method Theoretical study linking concentration of measure to adversarial vulnerability, focusing on Levy families and product spaces.
result Adversarial attacks can be made effective with minimal perturbations when test instances are concentrated.
New link detection results using closures of 3-braids.
problem Link detection using homology theories.
method Closure operations on 3-braids and homology theories.
result Detection of specific links using link Floer homology, Khovanov homology, and annular Khovanov homology.
Deep learning improves anomaly detection across various fields.
problem Detecting anomalies in data with advanced approaches.
method Survey of deep learning methods for anomaly detection.
result Advancements in deep anomaly detection address unique challenges.
Flexible pipeline for 3D vehicle detection from 2D images.
problem Current methods lack 3D perception of vehicles and other objects.
method Adopt any 2D detection network, fuse with 3D point cloud, develop model fitting algorithm, refine with CNN.
result 3D detection results rank second among algorithms, demonstrating competencies.
Deep learning improves combustor anomaly detection in gas turbines.
problem Improving anomaly detection performance in gas turbine combustors.
method Hierarchically learned features from exhaust gas temperature sensor measurements using deep learning.
result Deep learning-based anomaly detection significantly improved combustor anomaly detection performance.
Paper proposes RL for real-time smart grid cyber attack detection.
problem Real-time detection of cyber-attacks in smart grids.
method Formulated as POMDP, uses model-free reinforcement learning.
result Effective in timely and accurate detection of cyber-attacks.
Graph energy helps detect communities in networks better than traditional methods.
problem Detecting communities in sparse networks where traditional methods fail.
method Using graph energy based on the full spectrum of adjacency matrices.
result The difference in graph energy between a planted partition model and an Erdős--Rényi network has a distinct transition at the detectability threshold.
New ML-based detection improves PMH signal detection in load-modulated MIMO systems.
problem Detecting PMH signals without prior CSI is challenging and computationally expensive.
method Proposes HEM-ML and HEM-KD schemes using EM and KD-tree for efficient detection.
result Achieves comparable detection results to optimal ML detector with reduced complexity.
Develops slope detection for 3-manifolds with torus boundaries.
problem Determining slopes on the boundary of 3-manifolds with torus boundaries.
method Introduces order-detection and representation-detection of slopes, proving their equivalence.
result Shows how slopes' behavior changes with cabling, improving previous results.
2DSig-Detect detects adversarial perturbations in images.
problem Adversarial attacks degrade image model performance.
method 2D-signature embedded semi-supervised framework using rough path theory.
result 2DSig-Detect outperforms other methods in detecting adversarial perturbations.
Develops a method to detect changes in linear systems with temporal correlations.
problem Detect abrupt changes in time series data with temporal correlations.
method Data-dependent threshold for online change point detection in linear dynamical systems.
result Achieves a pre-specified upper bound on the probability of false alarms and provides a finite-sample-based bound for detection probability.
FADO detects adversarial scenarios without stochastic methods.
problem Adversarial scenarios where stochastic methods are not applicable.
method Machine learning and online learning theory-based deterministic detection.
result FADO provides theoretical guarantees for detection performance.
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
problem Computational hardness of hypergraphic planted clique detection.
method No specific method mentioned; focuses on conjecturing equivalence.
result Equivalence of computational hardness between HPC and PC detection.
Real-time fuel leakage detection framework MOCPD improves accuracy.
problem Early detection of fuel leakage to prevent hazards and losses.
method Memory-based Online Change Point Detection (MOCPD) framework.
result MOCPD outperforms baseline methods in detection accuracy.
Study on detecting hierarchical community structures in networks.
problem Detecting hierarchical community structures in networks.
method Analysis of planted hierarchies of partitions in networks, identifying additional detectability phases.
result There are additional phases in which the presence of multiple consistent partitions can either help or hinder detection of hierarchical structures.
QuickStop detects misinformation quickly using Markov models.
problem Real-time detection of misinformation.
method Markov model for probabilistic information spreading; optimal stopping algorithm.
result QuickStop outperforms existing algorithms in accuracy and detection time.
Deep object detection improves mitotic nucleus detection in breast cancer biopsies.
problem Challenges in automated mitotic nucleus detection in breast cancer histopathological images.
method Adapted Mask R-CNN for deep object detection, initially selects candidate regions with maximum recall, refines them with multi-object loss function.
result Improved discrimination ability (F-score of 0.86) and significant precision (0.86) for mitotic nuclei compared to two-stage models.
ECAD detects anomalies without data exchangeability, improving traffic flow detection.
problem Detecting anomalies in spatio-temporal data with missing values.
method ECAD uses conformal prediction to wrap around any regression algorithm, controlling Type-I error without data exchangeability.
result ECAD outperforms other methods in detecting anomalous traffic flow.
S4ND detects lung nodules faster and more accurately.
problem Efficient lung nodule detection from CT scans.
method Single-Shot Single-Scale 3D Convolutional Neural Network (CNN) trained end-to-end.
result S4ND outperforms state-of-the-art methods in terms of efficiency and accuracy.
PAC-Wrap provides provable guarantees for semi-supervised anomaly detection.
problem Ensuring reliable anomaly detection in safety-critical applications.
method PAC-Wrap wraps around existing anomaly detection methods to provide PAC guarantees.
result PAC-Wrap effectively provides rigorous guarantees for various anomaly detectors.
This paper offers a distribution-free method for post-detection changepoint localization.
problem Locating the exact time of a change in distribution after a sequential detection procedure.
method A distribution-free framework using conformal test martingales for sequential change detection and post-detection inference.
result Valid post-detection coverage guarantees and non-asymptotic bounds on confidence set size.
The study defines backdoor detection in ML and proves its infeasibility.
problem Backdoor detection in machine learning systems.
method Formal statistical definition and analysis of feasibility.
result Backdoor detection is impossible except for very small alphabet sizes.