GenAI4ELab/papercli-papers huggingface dataset:
Provably Secure Image Robust Steganography via Cross-modal Error Correction
Authors: Yuang Qi; Kejiang Chen; Na Zhao; Zijin Yang; Weiming Zhang
Venue: AAAI 2025 (main)

Abstract: The rapid development of image generation models has facilitated the widespread dissemination of generated images on social networks, creating favorable conditions for provably secure image steganography. 
However, existing methods face issues such as low quality of generated images and lack of semantic control in the generation process.
To leverage provably secure steganography with more effective and high-performance image generation models, and to ensure that stego images can accurately extract secret messages even after being uploaded to social networks and subjected to lossy processing such as JPEG compression, we propose a high-quality, provably secure, and robust image steganography method based on state-of-the-art autoregressive (AR) image generation models using Vector-Quantized (VQ) tokenizers. 
Additionally, we employ a cross-modal error-correction framework that generates stego text from stego images to aid in restoring lossy images, ultimately enabling the extraction of secret messages embedded within the images. Extensive experiments have demonstrated that the proposed method provides advantages in stego quality, embedding capacity, and robustness, while ensuring provable undetectability.
Medical Manifestation-Aware De-Identification
Authors: Yuan Tian; Shuo Wang; Guangtao Zhai
Venue: AAAI 2025 (main)

Abstract: Face de-identification (DeID) has been widely studied for common scenes, but remains under-researched for medical scenes, mostly due to the lack of large-scale patient face datasets. In this paper, we release MeMa, consisting of over 40,000 photo-realistic patient faces. MeMa is re-generated from massive real patient photos. By carefully modulating the generation and data-filtering procedures, MeMa avoids breaching real patient privacy, while ensuring rich and plausible medical manifestations. We recruit expert clinicians to annotate MeMa with both coarse- and fine-grained labels, building the first medical-scene DeID benchmark. Additionally, we propose a baseline approach for this new medical-aware DeID task, by integrating data-driven medical semantic priors into the DeID procedure. Despite its conciseness and simplicity, our approach substantially outperforms previous ones.
FROC: Building Fair ROC from a Trained Classifier
Authors: Avyukta Manjunatha Vummintala; Shantanu Das; Sujit Gujar
Venue: AAAI 2025 (main)

Abstract: This paper considers the problem of fair probabilistic binary classification with binary protected groups. The classifier assigns scores, and a practitioner predicts labels using a certain cut-off threshold based on the desired trade-off between false positives vs. false negatives. It derives these thresholds from the ROC of the classifier. The resultant classifier may be unfair to one of the two protected groups in the dataset. It is desirable that no matter what threshold the practitioner uses, the classifier should be fair to both the protected groups; that is, the ℒₚ norm between FPRs and TPRs of both the protected groups should be at most ε. We call such fairness on ROCs of both the protected attributes εₚ-Equalized ROC. Given a classifier not satisfying ε₁-Equalized ROC, we aim to design a post-processing method to transform the given (potentially unfair) classifier's output (score) to a suitable randomized yet fair classifier. That is, the resultant classifier must satisfy ε₁-Equalized ROC. First, we introduce a threshold query model on the ROC curves for each protected group. The resulting classifier is bound to face a reduction in AUC. With the proposed query model, we provide a rigorous theoretical analysis of the minimal AUC loss to achieve ε₁-Equalized ROC. To achieve this, we design a linear time algorithm, namely FROC, to transform a given classifier's output to a probabilistic classifier that satisfies ε₁-Equalized ROC. We prove that under certain theoretical conditions, FROC achieves the theoretical optimal guarantees. We also study the performance of our FROC on multiple real-world datasets with many trained classifiers.
Operationalising Rawlsian Ethics for Fairness in Norm Learning Agents
Authors: Jessica Woodgate; Paul Marshall; Nirav Ajmeri
Venue: AAAI 2025 (main)

Abstract: Social norms are standards of behaviour common in a society. However, when agents make decisions without considering how others are impacted, norms can emerge that lead to the subjugation of certain agents. We present RAWL·E, a method to create ethical norm-learning agents. RAWL·E agents operationalise maximin, a fairness principle from Rawlsian ethics, in their decision-making processes to promote ethical norms by balancing societal well-being with individual goals. We evaluate RAWL·E agents in simulated harvesting scenarios. We find that norms emerging in RAWL·E agent societies enhance social welfare, fairness, and robustness, and yield higher minimum experience compared to those that emerge in agent societies that do not implement Rawlsian ethics.
FairTP: A Prolonged Fairness Framework for Traffic Prediction
Authors: Jiangnan Xia; Yu Yang; Jiaxing Shen; Senzhang Wang; Jiannong Cao
Venue: AAAI 2025 (main)

Abstract: Traffic prediction is pivotal in intelligent transportation systems.  Existing works focus mainly on improving overall accuracy, overlooking a crucial problem of whether prediction results will lead to biased decisions by transportation authorities.  In practice, the uneven deployment of traffic sensors in different urban areas produces imbalanced data, making the traffic prediction model fail in some urban areas and leading to unfair regional decision-making that eventually severely affects equity and quality of residents’ life.  Existing fairness machine learning models struggle to maintain fair traffic prediction over prolonged periods.  Although these models might achieve fairness at certain time slots, this static fairness will break down as traffic conditions change.  To fill this research gap, we investigate prolonged fair traffic prediction, introducing two novel fairness metrics, i.e., region-based static fairness and sensor-based dynamic fairness, tailored to fairness fluctuations over time and across areas.  An innovative prolonged fairness traffic prediction framework, namely FairTP, is then proposed. FairTP achieves prolonged fairness by alternating between “sacrifice” and “benefit” the prediction accuracy of each traffic sensor or area, ensuring that the number of these two actions are balanced over time.     Specifically, FairTP incorporates a state identification module to discriminate whether the traffic sensors or areas are in a “sacrifice” or “benefit” state, thereby enabling prolonged fairness-aware traffic predictions.  Additionally, we devise a state-guided balanced sampling strategy to select training examples to further enhance prediction fairness by mitigating the performance disparities among areas with uneven sensor distribution over time.  Extensive experiments in two real-world datasets show that FairTP significantly improves prediction fairness without causing significant accuracy degradation.
Measuring Human and AI Values Based on Generative Psychometrics with Large Language Models
Authors: Haoran Ye; Yuhang Xie; Yuanyi Ren; Hanjun Fang; Xin Zhang; Guojie Song
Venue: AAAI 2025 (main)

Abstract: Human values and their measurement are long-standing interdisciplinary inquiry. Recent advances in AI have sparked renewed interest in this area, with large language models (LLMs) emerging as both tools and subjects of value measurement. This work introduces Generative Psychometrics for Values (GPV), an LLM-based, data-driven value measurement paradigm, theoretically grounded in text-revealed selective perceptions. The core idea is to dynamically parse unstructured texts into perceptions akin to static stimuli in traditional psychometrics, measure the value orientations they reveal, and aggregate the results. Applying GPV to human-authored blogs, we demonstrate its stability, validity, and superiority over prior psychological tools. Then, extending GPV to LLM value measurement, we advance the current art with 1) a psychometric methodology that measures LLM values based on their scalable and free-form outputs, enabling context-specific measurement; 2) a comparative analysis of measurement paradigms, indicating response biases of prior methods; and 3) an attempt to bridge LLM values and their safety, revealing the predictive power of different value systems and the impacts of various values on LLM safety. Through interdisciplinary efforts, we aim to leverage AI for next-generation psychometrics and psychometrics for value-aligned AI.
Neural Control and Certificate Repair via Runtime Monitoring
Authors: Emily Yu; Đorđe Žikelić; Thomas A. Henzinger
Venue: AAAI 2025 (main)

Abstract: Learning-based methods provide a promising approach to solving highly non-linear control tasks that are often challenging for classical control methods. To ensure the satisfaction of a safety property, learning-based methods jointly learn a control policy together with a certificate function for the property. Popular examples include barrier functions for safety and Lyapunov functions for asymptotic stability. While there has been significant progress on learning-based control with certificate functions in the white-box setting, where the correctness of the certificate function can be formally verified, there has been little work on ensuring their reliability in the black-box setting where the system dynamics are unknown. In this work, we consider the problems of certifying and repairing neural network control policies and certificate functions in the black-box setting. We propose a novel framework that utilizes runtime monitoring to detect system behaviors that violate the property of interest under some initially trained neural network policy and certificate. These violating behaviors are used to extract new training data, that is used to re-train the neural network policy and the certificate function and to ultimately repair them. We demonstrate the effectiveness of our approach empirically by using it to repair and to boost the safety rate of neural network policies learned by a state-of-the-art method for learning-based control on two autonomous system control tasks.
RUNA: Object-Level Out-of-Distribution Detection via Regional Uncertainty Alignment of Multimodal Representations
Authors: Bin Zhang; Jinggang Chen; Xiaoyang Qu; Guokuan Li; Kai Lu; Jiguang Wan; Jing Xiao; Jianzong Wang
Venue: AAAI 2025 (main)

Abstract: Enabling object detectors to recognize out-of-distribution (OOD) objects is vital for building reliable systems. A primary obstacle stems from the fact that models frequently do not receive supervisory signals from unfamiliar data, leading to overly confident predictions regarding OOD objects. Despite previous progress that estimates OOD uncertainty based on the detection model and in-distribution (ID) samples, we explore using pre-trained vision-language representations for object-level OOD detection. We first discuss the limitations of applying image-level CLIP-based OOD detection methods to object-level scenarios. Building upon these insights, we propose RUNA, a novel framework that leverages a dual encoder architecture to capture rich contextual information and employs a regional uncertainty alignment mechanism to distinguish ID from OOD objects effectively. We introduce a few-shot fine-tuning approach that aligns region-level semantic representations to further improve the model's capability to discriminate between similar objects. Our experiments show that RUNA substantially surpasses state-of-the-art methods in object-level OOD detection, particularly in challenging scenarios with diverse and complex object instances.
CL-Attack: Textual Backdoor Attacks via Cross-Lingual Triggers
Authors: Jingyi Zheng; Tianyi Hu; Tianshuo Cong; Xinlei He
Venue: AAAI 2025 (main)

Abstract: Backdoor attacks significantly compromise the security of large language models by triggering them to output specific and controlled content. Currently, triggers for textual backdoor attacks fall into two categories: fixed-token triggers and sentence-pattern triggers. However, the former are typically easy to identify and filter, while the latter, such as syntax and style, do not apply to all original samples and may lead to semantic shifts. In this paper, inspired by cross-lingual (CL) prompts of LLMs in real-world scenarios, we propose a higher-dimensional trigger method at the paragraph level, namely CL-Attack. CL-Attack injects the backdoor by using texts with specific structures that incorporate multiple languages, thereby offering greater stealthiness and universality compared to existing backdoor attack techniques.  Extensive experiments on different tasks and model architectures demonstrate that CL-Attack can achieve nearly 100 percents attack success rate with a low poisoning rate in both classification and generation tasks. We also empirically show that CL-Attack is more robust against current major defense methods compared to baseline backdoor attacks. Additionally, in response to CL-Attack, we further develop a new defense called TranslateDefense, which can partially mitigate the impact of CL-Attack.
LLM Agents Can Be Choice-Supportive Biased Evaluators: An Empirical Study
Authors: Nan Zhuang; Boyu Cao; Yi Yang; Jing Xu; Mingda Xu; Yuxiao Wang; Qi Liu
Venue: AAAI 2025 (main)

Abstract: With Large Language Model (LLM) agents taking on more evaluation responsibilities in decision-making, it is essential to recognize their possible biases to guarantee fair and trustworthy AI-supported decisions. This study is the first to thoroughly examine the choice-supportive bias in LLM agents, a cognitive bias that is known to impact human decision-making and evaluation. We conduct experiments across 19 open/unopen-source LLM models in five scenarios at maximum, employing both memory-based and evaluation-based tasks adapted and redesigned from human cognitive studies. Our findings show that LLM agents may exhibit biased attribution or evaluation that supports their initial choices, and such bias may persist even if contextual hallucination is not observable. Key findings show that bias manifestation can differ greatly depending on prompt construction and context preservation, and the bias may be mitigated in larger models. Significantly, we observe that the bias increases when the agents perceive they are in control. Our extensive study involving 284 well-educated humans shows that, despite bias, certain LLM agents can still perform better than humans in similar evaluation tasks. This research contributes to the growing area of AI psychology, and the findings underscore the importance of addressing cognitive biases in LLM Agent systems, with wide-ranging implications spanning from improving AI-assisted decision-making to advancing AI safety and ethics.
Privacy-and-Utility-Aware Publishing of Schedules
Authors: Maike Basmer; Stephan A. Fahrenkrog-Petersen; Ali Kaan Tutak; Arik Senderovich; Matthias Weidlich
Venue: AAAI 2025 (main)

Abstract: Scheduling is adopted in various domains to assign jobs to resources, such that an objective is optimized. While schedules enable the analysis of the underlying system, publishing them also incurs a privacy risk. Recently, privacy attacks on schedules have been proposed, which may reveal sensitive information on the jobs by solving an inverse scheduling problem. In this work, we study the protection against such attacks. We formulate the problem of privacy-and-utility preservation of schedules, which bounds both, the privacy leakage and the loss in the utility of the schedule due to obfuscation. We address the problem based on a set of perturbation functions for schedules, study their instantiations for standard scheduling problems, and implement privacy-and-utility-aware publishing of a schedule using constraint programming. Experiments with synthetic and real-world schedules demonstrate the feasibility, robustness, and effectiveness of our mechanism.
Revelations: A Decidable Class of POMDPs with Omega-Regular Objectives
Authors: Marius Belly; Nathanaël Fijalkow; Hugo Gimbert; Florian Horn; Guillermo A. Pérez; Pierre Vandenhove
Venue: AAAI 2025 (main)

Abstract: Partially observable Markov decision processes (POMDPs) form a prominent model for uncertainty in sequential decision making. We are interested in constructing algorithms with theoretical guarantees to determine whether the agent has a strategy ensuring a given specification with probability 1. This well-studied problem is known to be undecidable already for very simple omega-regular objectives, because of the difficulty of reasoning on uncertain events. We introduce a revelation mechanism which restricts information loss by requiring that almost surely the agent has eventually full information of the current state. Our main technical results are to construct exact algorithms for two classes of POMDPs called weakly and strongly revealing. Importantly, the decidable cases reduce to the analysis of a finite belief-support Markov decision process. This yields a conceptually simple and exact algorithm for a large class of POMDPs.
Efficient Fault-Tolerant Search by Fast Indexing of Subnetworks
Authors: Davide Bilò; Keerti Choudhary; Sarel Cohen; Tobias Friedrich; Martin Schirneck
Venue: AAAI 2025 (main)

Abstract: We design sensitivity oracles for error-prone networks. For a network problem Π, the data structure preprocesses a network G=(V,E) and sensitivity parameter f such that, for any set F of up to f link or node failures, it can report the solution of Π in G-F. We study three network problems Π.
- L-Hop Shortest Path: Given s,t in V, is there a shortest s-t-path in G-F with at most L links? 
- k-Path: Does G-F contain a simple path with k links?
- k-Clique: Does G-F contain a clique of k nodes?

Our main technical contribution is a new construction of (L,f)-replacement path coverings ((L,f)-RPC) in the parameter realm where f = o(log L). An  (L,f)-RPC is a family G' of subnetworks of G which, for every set F of at most f links, has a subfamily G'_F such that (i) no subnetwork in G'_F contains a link of F and (ii) for each s,t in V, if G-F contains a shortest s-t-path with at most L links, then some subnetwork in G'_F retains at least one such path. Our (L,f)-RPC has almost the same size as the one by Weimann and Yuster (2013) but it improves the time to query G'_F from Õ(f^2 L^f) to Õ(f^(5/2) L^o(1)). It also improves over the size and query time of the (L,f)-RPC by Karthik and Parter (2021) by nearly a factor of L. From this construction, we derive oracles for L-Hop Shortest Path, k-Path, and k-Clique. Notably, our solution for k-Path improves the query time of the one by Bilò  for f=o(log k).
Towards Practical Classical Planning Compilations of Numeric Planning
Authors: Luigi Bonassi; Francesco Percassi; Enrico Scala
Venue: AAAI 2025 (main)

Abstract: It is well known that numeric planning can be made decidable if the domain of all numeric state variables is finite. This bounded formulation can be polynomially compiled into classical planning with Boolean conditions and conditional effects preserving the plan size exactly. However, it remains unclear whether this compilation has any practical utility. To explore this aspect, this work revisits the theoretical compilation framework from a practical perspective, focusing on the fragment of simple numeric planning. Specifically, we introduce three different compilations. The first, called one-hot, aims to systematise the current practice among planning practitioners of modelling numeric planning through classical planning. The other two, termed binary compilations, extend and specialise the logarithmic encoding introduced in previous literature. Our experimental analysis reveals that the overly complex logarithmic encoding can, surprisingly, be made practical with some representational expedients. Among these, the use of axioms is particularly crucial. Furthermore, we identify a class of mildly numeric planning problems where a classical planner, i.e., LAMA, when run on the compiled problem, is highly competitive with state-of-the-art numeric planners.
Temporal Numeric Planning with Patterns
Authors: Matteo Cardellini; Enrico Giunchiglia
Venue: AAAI 2025 (main)

Abstract: We consider temporal numeric planning problems Π expressed in PDDL2.1, and show how it is possible to produce SMT formulas (i) whose models correspond to valid plans of Π, and (ii) which extends the recently proposed planning with patterns approach from the numeric to the temporal case. We prove the correctness and completeness of the approach and that it outperforms all the publicly available temporal planners on 10 domains with required concurrency.
Approximating Optimal Labelings for Temporal Connectivity
Authors: Daniele Carnevale; Gianlorenzo D'Angelo; Martin Olsen
Venue: AAAI 2025 (main)

Abstract: In a temporal graph the edge set dynamically changes over time according to a set of time-labels associated with each edge that indicates at which time-step the edge is available. Two vertices are connected if there is a path connecting them in which the edges are traversed in increasing order of their labels. We study the problem of scheduling the availability time of the edges of a temporal graph in such a way that all pairs of vertices are connected within a given maximum allowed time a and the overall number of labels is minimum.

The problem, called Minimum Aged Labeling (MAL), has several applications in logistics, distribution scheduling, and information spreading in social networks, where carefully choosing the time-labels can significantly reduce infrastructure costs, fuel consumption, or greenhouse gases.

Problem MAL has previously been proved to be NP-complete on undirected graphs and APX-hard on directed graphs. 
In this paper, we extend our knowledge on the complexity and approximability of MAL in several directions. We first show that the problem cannot be approximated within a factor better than O(log n) when a >= 2, unless P = NP, and a factor better than 2^[log^(1-ε) n] when a >= 3, unless NP is contained in DTIME(2^(polylog(n))), where n is the number of vertices in the graph. Then we give a set of approximation algorithms that, under some conditions, almost match these lower-bounds. In particular, we show that the approximation depends on a relation between a and the diameter of the input graph.

We further establish a connection with a foundational optimization problem on static graphs called Diameter Constrained Spanning Subgraph (DCSS) and show that our hardness results also apply to DCSS.
How Many Lines to Paint the City: Exact Edge-Cover in Temporal Graphs
Authors: Argyrios Deligkas; Michelle Döring; Eduard Eiben; Tiger-Lily Goldsmith; George Skretas; Georg Tennigkeit
Venue: AAAI 2025 (main)

Abstract: Logistics and transportation networks require a large amount of resources to realise necessary connections between locations and minimizing these resources is a vital aspect of planning research. 
Since such networks have dynamic connections that are only available at specific times, intricate models are needed to portray them accurately. 
In this paper, we study the problem of minimizing the number of resources needed to realise a dynamic network, using the temporal graphs model.
In a temporal graph, edges appear at specific points in time.
Given a temporal graph and a natural number k, we ask whether we can cover every temporal edge exactly once using at most k temporal journeys; in a temporal journey consecutive edges have to adhere to the order of time.
We conduct a thorough investigation of the complexity of the problem with respect to four dimensions: 
(a) whether the type of the temporal journey is a walk, a trail, or a path; 
(b) whether the chronological order of edges in the journey is strict or non-strict; 
(c) whether the temporal graph is directed or undirected; 
(d) whether the start and end points of each journey are given.
We almost completely resolve the complexity of these problems and provide dichotomies for each of them with respect to k.