arXiv subjects

Hongcheng Li

Publications and source records attributed to Hongcheng Li.

8 recordsLinked to original sources

arXiv2026

Hidden Commitment Power is Powerless

A principal who offers a contract may renege when her default option is sufficiently attractive. The size of this temptation, which measures her commitment power, is often her private information. This paper asks how contracting outcomes change under this information asymmetry. Disciplining off-path beliefs with the Intuitive Criterion, I find that every type of principal behaves and earns payoffs exactly as if she were commonly known to have the least commitment power. Hidden commitment power is therefore powerless. Commitment power is valuable under symmetric information. Concealing it is what destroys its value. The result delivers an unambiguous policy lesson on how to mitigate this information asymmetry prior to contracting: only measures that improve the worst case have value. Applied to credit rating, optimal disclosure is monotone-partitional, and each issuer contracts on the floor of her rating band.

econ.TH↗

arXiv2026

Backdoor Sentinel: Detecting and Detoxifying Backdoors in Diffusion Models via Temporal Noise Consistency

Diffusion models have been widely deployed in AIGC services, but their reliance on opaque training data exposes them to backdoor attacks. In practical auditing scenarios, auditors are typically unable to access model parameters due to intellectual property protection, making white-box or query-intensive detection impractical. After detection, existing detoxification approaches are trapped in a dilemma between detoxification effectiveness and generation quality for service providers. We reveal Temporal Noise Consistency (TNC), a previously unreported phenomenon in which backdoor activation disrupts the consistency of noise predictions between adjacent diffusion timesteps within specific temporal segments, while clean inputs remain stable. Based on this finding, we propose TNC-Defense, a closed-loop framework for gray-box backdoor detection and model repair. Specifically, TNC-Detect (for auditors) uses inference-stage adjacent-noise statistics to detect backdoors and precisely localize anomalous timesteps without model-weight access. TNC-Detox (for service providers) utilizes these locations to perform trigger-agnostic, timestep-aware correction of the generation path, suppressing backdoor behavior while reducing detoxification cost. Across five representative backdoor attacks and state-of-the-art defenses, TNC-Defense improves the average detection accuracy by $11\%$ with negligible additional overhead, and invalidates an average of $98.5\%$ of triggered samples with only a mild degradation in generation quality. Our code is publicly available at: https://github.com/binzhwang/TNC-Defense.

cs.CR↗

arXiv2025

Contracting against Non-contractible Outsider

This paper studies contracting in the presence of externalities with a non-contractible outsider. Multiple equilibria arise from strategic symmetry between the insider agent and the outsider. To address strategic uncertainty, the principal guarantees their actions in a unique equilibrium. A novel duality approach reformulates her problem as a series of problems in which she selects agent expectations. The key constraint is that the principal cannot convince the agent to expect non-guaranteed response from the outsider. Due to strategic rents, the principal optimally induces attenuated agent incentives. With completely symmetric strategic dependence, her coordination and commitment power become perfect substitutes; in addition, public contracting can strictly decrease her surplus compared to private contracting, in sharp contrast with the case where she ignores robustness. Applications include regulating international competition, platform design, and labor union contracting.

econ.TH↗

arXiv2025

Robust Contracting with Career Concerns

We study optimal contracting when workers face career concerns. Labor markets infer ability from performance, but effort affects how informative performance is. This feedback can generate strategic uncertainty: bonuses inducing effort under optimistic beliefs about effort may fail under pessimistic beliefs. We characterize this force through a criterion tied to skill-effort complementarity and solve for the least-cost policy implementing effort in every equilibrium. Under strategic uncertainty, the employer uses dispersed bonuses. High bonuses rule out pessimistic beliefs, raising the reputational stakes and letting lower bonuses motivate effort. Pay dispersion among observationally identical workers grows with career concerns and skill-wage assortativeness.

econ.TH↗

arXiv2024

Robust Pricing for Quality Disclosure

A platform charges a producer for disclosing quality evidence to consumers before trade. It aims to maximize its revenue guarantee across potentially multiple equilibria which arise from the interdependence of producer purchase decisions and consumer beliefs. The platform's optimal pricing strategy entrenches itself as a market gatekeeper: it induces a unique equilibrium in which non-disclosed products' perceived values are lower than the production cost. To achieve this goal, this pricing strategy iteratively destabilizes under-disclosure equilibria by luring producers to disclose slightly more. Higher-quality producers receive higher rents as their disclosure is prioritized. Despite losing rents, the platform optimally induces socially efficient information transmission for any given evidence structure, and it never benefits from garbling evidence. Compared to the non-robust benchmark, our framework generates more intuitive comparative statics: the platform's ability to extract surplus increases with its value as an information intermediary.

econ.TH↗

arXiv2023

Multiplayer War of Attrition with Asymmetric Private Information

This paper studies a war of attrition game in the setting of public good provision that combines three elements: (i) multiple players, (ii) incomplete information, and (iii) ex-ante asymmetry. In the unique equilibrium, asymmetry leads to a stratified behavior pattern where one player provides the public good instantly with a positive probability while each of the other players has a player-specific strict waiting time, before which even his highest type will not provide the good. Comparative statics show that a player with less patience, lower cost of provision, and higher reputation in value (expressed in a form of hazard rate) provides the good type-wise uniformly faster. In large societies, the cost of delay is mainly determined by the highest type of the instant-exit player.

econ.TH↗

arXiv2019

Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction

Recently, many studies have demonstrated deep neural network (DNN) classifiers can be fooled by the adversarial example, which is crafted via introducing some perturbations into an original sample. Accordingly, some powerful defense techniques were proposed. However, existing defense techniques often require modifying the target model or depend on the prior knowledge of attacks. In this paper, we propose a straightforward method for detecting adversarial image examples, which can be directly deployed into unmodified off-the-shelf DNN models. We consider the perturbation to images as a kind of noise and introduce two classic image processing techniques, scalar quantization and smoothing spatial filter, to reduce its effect. The image entropy is employed as a metric to implement an adaptive noise reduction for different kinds of images. Consequently, the adversarial example can be effectively detected by comparing the classification results of a given sample and its denoised version, without referring to any prior knowledge of attacks. More than 20,000 adversarial examples against some state-of-the-art DNN models are used to evaluate the proposed method, which are crafted with different attack techniques. The experiments show that our detection method can achieve a high overall F1 score of 96.39% and certainly raises the bar for defense-aware attacks.

cs.CR↗

arXiv2019

Deep Text Classification Can be Fooled

In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack. Specifically, confronted with different adversarial scenarios, the text items that are important for classification are identified by computing the cost gradients of the input (white-box attack) or generating a series of occluded test samples (black-box attack). Based on these items, we design three perturbation strategies, namely insertion, modification, and removal, to generate adversarial samples. The experiment results show that the adversarial samples generated by our method can successfully fool both state-of-the-art character-level and word-level DNN-based text classifiers. The adversarial samples can be perturbed to any desirable classes without compromising their utilities. At the same time, the introduced perturbation is difficult to be perceived.

cs.CR↗