Human-AI Adversarial Challenge

• Human-AI adversarial challenge is a framework combining adversarial attacks and human cognitive evaluations to assess AI robustness.
• It employs dual methods by crafting examples that fool AI models while remaining imperceptible to human detection using suspiciousness ratings.
• It integrates human factors into adversarial strategies, paving the way for adaptive defenses and trust-enhanced decision-making in AI systems.

The Human–AI Adversarial Challenge encompasses a class of tasks, methodologies, and evaluation frameworks designed to rigorously probe, quantify, and ultimately fortify the robustness of AI systems in settings involving direct or indirect interaction with humans. While early adversarial analysis focused on pure model performance under synthetic perturbations, the contemporary paradigm explicitly incorporates human cognitive factors, perceptual constraints, and behavioral dynamics. This field is now characterized by dual objectives: (1) the construction of adversarial examples that both fool or degrade model performance and evade human detection, and (2) the development of new human–AI benchmarks and defense mechanisms that leverage human judgment or human-in-the-loop verification as essential components of overall system robustness.

1. Conceptual Distinctions: Imperceptibility Versus Human Suspiciousness

Traditional adversarial attacks in the image domain rely on imperceptibility, seeking perturbations $\delta$ such that $x' = x+\delta$ satisfies $\|\delta\| < \epsilon$ and $x'$ is visually indistinguishable from $x$ to a human observer. In contrast, adversarial attacks on text—either for detection evasion or for NLP task manipulation—must account for two discrete constraints: semantic similarity and the discrete, context-dependent nature of tokens. Recent work establishes “human suspiciousness” as a construct that is distinct from imperceptibility: an adversarial text $t'$ is effective not only when it evades model detection, but also when human annotators find it non-suspicious, i.e., assign a low suspiciousness rating on a Likert scale (Tonni et al., 6 Oct 2024).

This distinction is operationalized in both annotation protocols (human raters grade each adversarial text’s suspiciousness independently) and evaluation objectives (generation algorithms are increasingly optimized to minimize both detection probability and suspiciousness score).

2. Formal Frameworks for Modeling Human Factors in Adversarial Analysis

Contemporary adversarial robustness models in human–AI decision-making systems now include explicit mathematical representations of human trust, expectations, and override behavior (Fan et al., 25 Sep 2025). In Fan et al., a dual-assessment framework is implemented:

• Reliance assessment: The human’s reliance $r_i$ on AI at decision $T_i$ is modeled as

$r_i = \gamma D_i + (1-\gamma) I_i;$

where $D_i$ is the performance feedback and $I_i$ is an aggregate of model-irrelevant human factors (self-confidence, risk, complexity, time sensitivity).

• History smoothing: A momentum update reflects trust inertia:

$r_{i+1}^* = \alpha r_i^* + (1-\alpha) r_{i+1}.$

• Trust gating / override: A threshold $\hat{r}$ determines AI versus human execution per task.
• Attack score modification: The overall attack score aggregates failures only after human trust and possible override are processed.

This representation facilitates analysis of “attack timing” effects, showing that sparsely timed perturbations can maximize adversarial impact by first undermining trust, then exploiting synchronous trust recovery for later attacks.

3. Human Suspiciousness-Graded Datasets and Annotation Protocols

To empirically link adversarial text suspiciousness to real-world detectability, researchers now collect and release large-scale human annotation sets, such as Likert-scale graded suspiciousness datasets (Tonni et al., 6 Oct 2024). Protocols typically include:

• Sentence-level evaluation: Human raters score sentences generated by four well-known attack methods for suspiciousness, both in blind and non-blind conditions.
• Statistical aggregation: Inter-annotator agreement (e.g., Fleiss’ kappa) is computed to validate label consistency.
• Correlational analysis: Suspiciousness scores are compared to direct measures of human detection (binary judgments or ROC curves) and to detector outputs to establish ground-truth baselines for future adversarial text generation models.

Such datasets enable both descriptive analysis (what attack patterns appear suspicious?) and prescriptive optimization (how to condition attack algorithms to minimize suspiciousness?).

4. Regression-Based Suspiciousness Modeling and Optimized Adversarial Text Generation

To quantify and operationalize human suspiciousness, regression-based models are trained on annotated data to predict suspiciousness scores for candidate adversarial texts (Tonni et al., 6 Oct 2024). Architectures typically involve:

• Feature extraction: Token-level and sentence-level features (e.g. n-gram statistics, perplexity, syntactic divergence) are fed into a regression head.
• Loss function: Mean squared error (MSE) between predicted and human-annotated suspiciousness grades.
• Training regime: Supervised learning using the annotated corpus.

Adversarial text generation algorithms are then augmented with a suspiciousness regularization term. Given a base attack objective $\mathcal{L}_{attack}$ (e.g., maximizing classifier loss or detection evasion), the generator’s overall objective is

$$\mathcal{L}_{total} = \mathcal{L}_{attack} + \lambda \cdot \mathcal{L}_{suspiciousness}$$

where $\mathcal{L}_{suspiciousness}$ penalizes outputs with high regression-predicted suspiciousness. Hyperparameter $\lambda$ is tuned to balance misclassification success against imperceptibility.

5. Attack Methodologies and Trade-Offs

Recent benchmarks demonstrate that four canonical adversarial attack methods—synonym replacement, gradient-based paraphrasing, white-box mask-and-infill, and combined semantic–syntactic rewriting—display markedly different suspiciousness profiles (Tonni et al., 6 Oct 2024):

Attack Method	Semantic Fidelity Constraint	Typical Suspiciousness Score	Detection Evasion Rate
Synonym Swap	High	Moderate–High	Low–Moderate
Mask-and-Infill (MLM)	Medium	Low	Moderate
Gradient Paraphrase	High	Low	High
Semantic–Syntactic	Variable	Variable	High

Empirical studies reveal that attacks tuned only for classifier evasion or statistical metrics (such as perplexity minimization) are not always well aligned with low human suspiciousness. Conversely, suspiciousness-aware attacks incur minor semantic degradation but substantially reduce the likelihood of human detection.

6. Integration of Human Suspiciousness into Adversarial Generation Objectives

State-of-the-art adversarial text generators now explicitly integrate regressor-computed suspiciousness into their objective functions (Tonni et al., 6 Oct 2024). The workflow is as follows:

1. For each candidate adversarial text during search or decoding, compute its suspiciousness score using the trained regressor.
2. Accept/reject or rescore candidates based on a compound criterion: only texts that both flip the model decision and fall below a suspiciousness threshold are considered viable.
3. Empirical validation shows that incorporating suspiciousness scores reduces the fraction of adversarial texts labeled as “machine-generated” by humans, while maintaining effective model evasion rates.

A plausible implication is that adversarial robustness countermeasures for NLP systems will need to jointly optimize for semantic preservation, human-like statistical properties, and adversarial resistance under both detector and evaluator models.

7. Limitations, Open Questions, and Future Directions

Current regression models for suspiciousness, while predictive on held-out annotated data, are subject to distributional shift and annotator variability. There is active discussion on:

• Personalization: Developing user-specific suspiciousness models that account for individual detector idiosyncrasies or domain expertise.
• Generalizability: Extending suspiciousness modeling to multilingual text, longer document spans, and stylometric features.
• Certified Robustness: Theoretical bounds on maximum allowable perturbations—analogous to $\ell_p$ certificates in vision—that guarantee human-level imperceptibility.

As datasets, annotation protocols, and modeling become more sophisticated, future human–AI adversarial challenges are anticipated to feature adaptive benchmarks, meta-adversary ensembles, and joint optimization of both technical and social robustness. The entrenched role of human suspiciousness as a central adversarial constraint is now recognized as foundational in the design of interpretable, trustworthy, and resilient human–AI systems.