Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image wit…
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48 results for “signal manipulation”
Study cyber-attacks on RL algorithms, focusing on cost signal manipulation.
problem Adversarial manipulation of cost signals in reinforcement learning.
method Quantitative analysis of TD() and -learning algorithms under manipulation.
result Bound on approximation error for TD() and convergence properties for -learning under stealthy attacks.
New attack manipulates UCB algorithm, new defense algorithm reduces pseudo-regret.
problem Adversarial attacks on stochastic bandit algorithms.
method Introducing action-manipulation attacks and proposing a robust defense algorithm.
result Proposed defense algorithm reduces pseudo-regret to O(max{log T, A}).
Proposes a method to boost deep reinforcement learning with sparse rewards.
problem Challenges in learning complex behaviors with long horizons and sparse rewards.
method Predictive coding for reward shaping.
result Achieves better learning by providing reward signals that understand environment dynamics and emphasize useful features.
Can we manipulate multiple deep neural networks simultaneously?
problem Selective fooling of multiple machine learning systems.
method Formulated as a novel optimization problem.
result It is easy to selectively manipulate multiple MNIST classifiers simultaneously.
Paper introduces SCI to distinguish market signals from coordination.
problem Unclear signals in prediction markets.
method Formalizes SCI, introduces weighted and time-varying extensions.
result Discriminates between market signals and coordination.
In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness. In principle, one could apply image-based style transfer techniques t…
In the information-based approach to asset pricing the market filtration is modelled explicitly as a superposition of signals concerning relevant market factors and independent noise. The rate at which the signal is revealed to the market then determines the overall magnitude of asset volatility. By letting this inform…
Generative networks have made it possible to generate meaningful signals such as images and texts from simple noise. Recently, generative methods based on GAN and VAE were developed for graphs and graph signals. However, the mathematical properties of these methods are unclear, and training good generative models is di…
This paper tackles fair online decision-making in contextual bandits, achieving optimal performance and fairness.
problem Fairness in online decision-making systems under strategic manipulation.
method Develops algorithms for linear and smooth reward functions, maintaining fairness and optimal regret.
result Achieves nearly minimax-optimal regret with strong fairness guarantees, even in the presence of attacks.
This paper improves self-play learning in games by manipulating experience distributions.
problem Improving self-play learning in games through better experience sampling.
method Three approaches: weighted sampling, Prioritized Experience Replay, and diversifying trajectories.
result Major improvements in early training performance in some games, minor improvements overall.
The paper presents a novel approach of spoofing wireless signals by using a general adversarial network (GAN) to generate and transmit synthetic signals that cannot be reliably distinguished from intended signals. It is of paramount importance to authenticate wireless signals at the PHY layer before they proceed throug…
AIMM-X monitors markets for suspicious behavior using transparent scoring.
problem Detecting market manipulation from benign mechanisms.
method Combines microstructure signals and public attention signals for anomaly detection.
result Transparent scoring allows tracing and understanding flagged windows.
This study proposes a trainable adaptive window switching (AWS) method and apply it to a deep-neural-network (DNN) for speech enhancement in the modified discrete cosine transform domain. Time-frequency (T-F) mask processing in the short-time Fourier transform (STFT)-domain is a typical speech enhancement method. To re…
Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a function on the state space. However, the sparsity of such reward definition makes traditional reinforcement learning algorithms very inefficien…
Disentangling Controllable Object through Video Prediction Improves Visual Reinforcement Learningcs.LG
A framework disentangles controllable objects from visual signals for improved RL.
problem Improving sample efficiency and game performance in vision-based RL.
method Action-conditioned video prediction to disentangle controllable objects.
result Improved sample efficiency and game performance in Atari games.
This work improves RL for complex robotic tasks by guiding exploration with task-specific goal distributions.
problem Solving long-horizon, complex sequential tasks in robotics with sparse rewards.
method Extends hindsight relabelling to task-specific goal distributions using a small set of demonstrations.
result Significantly higher overall performance on complex robotic manipulation tasks.
DDSP integrates signal processing with deep learning for high-fidelity audio synthesis.
problem Efficiently combining signal processing knowledge with deep learning for audio synthesis.
method Integrates classic signal processing elements with deep learning methods.
result High-fidelity audio synthesis without large models or adversarial losses.
AI learns market manipulation through simulation, suggesting regulation.
problem Regulating AI to prevent market manipulation.
method Used a genetic algorithm in an artificial market simulation.
result AI discovered market manipulation as an optimal strategy.
Randomized classifiers improve strategic classification efficiency.
problem Designing optimal classifiers in strategic classification games.
method Investigation of randomized classifiers and their efficiency in strategic classification.
result Randomized classifiers are necessary for maximizing classification efficiency.
The paper analyzes how leverage affects manipulation in event-linked markets, offering new insights into regulation.
problem Manipulation and insider information in leveraged event-linked markets.
method Develops a two-axis manipulation taxonomy and analyzes leverage's effects on market-price and outcome manipulation.
result Leverage scales market-price manipulation linearly but shifts the cost-benefit threshold for outcome manipulation.
We introduce an adversarial method for producing high-recall explanations of neural text classifier decisions. Building on an existing architecture for extractive explanations via hard attention, we add an adversarial layer which scans the residual of the attention for remaining predictive signal. Motivated by the impo…
New model improves neural network robustness against input manipulations.
problem Improving neural network robustness against input manipulations.
method Causal view and deep causal manipulation augmented model (deep CAMA) with data augmentation and test-time fine-tuning.
result Deep CAMA shows superior robustness against unseen manipulations compared to traditional models.
A new reinforcement learning method uses mutual information to encourage agents to control their environment.
problem Learning from internal drives instead of external rewards.
method Formulate an intrinsic objective as mutual information between goal states and controllable states, derive a surrogate objective for efficient optimization.
result Demonstrated the efficacy of the approach in robotic tasks.
New method learns various data manipulation schemes for model training.
problem Improving model training with data manipulation.
method Adapts RL reward learning algorithm for data manipulation learning.
result Significant improvement in classification performance.
This paper studies poisoning attacks in episodic RL and discovers their effectiveness depends on reward bounds.
problem Understanding security threats to RL algorithms through poisoning attacks.
method Examined two types of poisoning attacks: reward and action manipulation, in bounded and unbounded reward settings.
result The effectiveness of poisoning attacks depends on reward bounds, with different attack costs and success rates.
Manipulating video content is easier than ever. Due to the misuse potential of manipulated content, multiple detection techniques that analyze the pixel data from the videos have been proposed. However, clever manipulators should also carefully forge the metadata and auxiliary header information, which is harder to do …
Self-guidance controls image generation by extracting properties from diffusion model representations.
problem Generating images from text descriptions is challenging due to the complexity of visual details.
method Self-guidance uses internal representations of diffusion models to control image generation.
result Properties like object shape, location, and appearance can be extracted and used to steer image generation.
We study adversarial attacks that manipulate the reward signals to control the actions chosen by a stochastic multi-armed bandit algorithm. We propose the first attack against two popular bandit algorithms: -greedy and UCB, \emph{without} knowledge of the mean rewards. The attacker is able to spend only logarithmic …
We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors - from scratch - in the presence of multiple sparse reward signals. To this end, the agent is equipped with a set of general auxiliary tasks, that it attempt…
Donors who defer their donations volunteer less in the future.
problem Volunteer labor can be less beneficial to charities than its costs.
method Regression discontinuity design with a procedure to handle manipulation.
result Donor manipulation invalidates standard regression discontinuity design, but a new method provides partial identification bounds.
In this work we propose a model that can manipulate individual visual attributes of objects in a real scene using examples of how respective attribute manipulations affect the output of a simulation. As an example, we train our model to manipulate the expression of a human face using nonphotorealistic 3D renders of a f…
Prediction markets can be manipulated by traders who can move contract settlements, harming price discovery.
problem Manipulation of settlement times in prediction markets leads to unfair wealth transfer and harms price discovery.
method Developed a model showing how settlement manipulation transfers wealth and harms price discovery, and observed real-world effects on Polymarket's Bitcoin contract.
result Manipulators capture significant profits from retail traders, especially when settlement times are short.
Adversaries manipulate wireless power allocation to reduce user rates.
problem Adversaries exploit deep learning for power control to decrease communication rates.
method Adversaries craft perturbations to inputs of a DNN to minimize power allocation.
result Adversarial attacks are highly effective and robust to uncertainties.
DIGIT is a low-cost tactile sensor for in-hand manipulation.
problem Difficulty in sensing contact forces limits robotic manipulation.
method DIGIT miniaturizes and improves a vision-based tactile sensor.
result DIGIT enables better control of interactions with the environment.
Paper presents a new port-Hamiltonian model for vehicle manipulators.
problem Complex mechanical systems' energy flow and conservation.
method Derives port-Hamiltonian dynamics from Hamiltonian reduction theory.
result Establishes mathematical equivalence with existing formulations.
New model for recovering unverifiable signals from observers in decentralized networks.
problem Verifying the level of service provided by untrusted parties in decentralized networks.
method Formal model of signal elicitation, source identifiability condition, and peer prediction techniques.
result Existence of a strictly truthful mechanism for unverifiable signal recovery.
Optimal benchmark design varies based on costs in financial manipulation.
problem Manipulation of price benchmarks in finance.
method Analyzes empirical pattern and cost structures to determine optimal benchmark design.
result The optimal benchmark depends on the relative sizes of fixed and variable costs.
Approximate Eigenvalue Decompositions of Linear Transformations with a Few Householder Reflectorsmath.NA
The ability to decompose a signal in an orthonormal basis (a set of orthogonal components, each normalized to have unit length) using a fast numerical procedure rests at the heart of many signal processing methods and applications. The classic examples are the Fourier and wavelet transforms that enjoy numerically effic…
Study examines reasons for Nutek India's share price drop.
problem Deep fall in Nutek India Limited's share price.
method Analyzed transactions and market forces to identify manipulation.
result Identified intentional interference in supply and demand.
The paper discusses decades of stock market manipulation and its benefits.
problem Manipulating public equity markets over 30 years.
method Quantitative analysis of market impact and price movement.
result Price manipulation is a valuable and profitable tool.
New method uses statistical physics to detect financial market manipulation.
problem Detecting financial market manipulation activities like spoofing and layering.
method Modeling order book dynamics as particle motion and using momentum measure.
result Method outperforms conventional Z-score-based anomaly detection.
Learning Unfair Trading: a Market Manipulation Analysis From the Reinforcement Learning Perspectiveq-fin.TR
Market manipulation is a strategy used by traders to alter the price of financial securities. One type of manipulation is based on the process of buying or selling assets by using several trading strategies, among them spoofing is a popular strategy and is considered illegal by market regulators. Some promising tools h…
New foundation for Shapley value immune to coalitional manipulations.
problem Coalitional manipulations in game theory.
method Revised Shapley value criteria for coalitional games.
result Shapley value is immune to coalitional manipulations under new criteria.
Extended Predictable Feature Analysis (PFAx) [Richthofer and Wiskott, 2017] is an extension of PFA [Richthofer and Wiskott, 2015] that allows generating a goal-directed control signal of an agent whose dynamics has previously been learned during a training phase in an unsupervised manner. PFAx hardly requires assumptio…
Framework detects covert financial market manipulation using LOB representations.
problem Detecting covert financial market manipulation (spoofing) from complex anomaly patterns in multilevel prices.
method Cascaded contrastive representation learning of LOB data.
result Transformer-based architectures achieve state-of-the-art results in detection performance.
New approach uses unlabeled prior data to accelerate exploration in sparse reward tasks.
problem Sparse reward tasks in reinforcement learning.
method Learn reward model from online experience, label prior data, and use concurrently.
result Rapid exploration in challenging sparse-reward domains.
Study reveals widespread manipulation of meme coins, leading to significant economic losses.
problem Widespread manipulation of meme coins leading to economic losses.
method Cross-chain analysis of 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base.
result 82.8% of high-return tokens show evidence of artificial growth strategies.