Adversarial Interference Simulation (AIS)

• Adversarial Interference Simulation (AIS) is a suite of methodologies that model, simulate, and exploit interference mechanisms to identify and counteract vulnerabilities across systems.
• AIS employs formal recipes for crafting adversarial attacks and designing defense architectures, leveraging multi-layer simulations to evaluate and enhance system robustness.
• Empirical and theoretical evaluations highlight predictable attack patterns, high transferability, and significant performance degradation in various applications, informing proactive secure system engineering.

Adversarial Interference Simulation (AIS) is a collective term for a suite of methodologies used to analyze, predict, and counteract vulnerabilities in systems—machine learning, sensing, communications, and control—by explicitly modeling, simulating, and algorithmically exploiting interference mechanisms. AIS provides not only formal recipes for constructing adversarial attacks by leveraging system encoding or exploitation of interference but also enables defense architecture design and system robustness evaluation via targeted multi-layer simulation.

1. Theoretical Foundation: Interference, Superposition, and Vulnerability

AIS was introduced as a mechanistic explanation for adversarial vulnerability in neural networks, motivated by feature encoding constraints. In “Adversarial Attacks Leverage Interference Between Features in Superposition,” superposition is defined as the representation of more semantic features ($M$) than latent dimensions ($m$), resulting in overcomplete, non-orthogonal latent directions $v_j \in \mathbb{R}^m$ and packed via $h \approx \sum_j a_j(x) v_j$ (Stevinson et al., 13 Oct 2025). When an input $x \in \mathbb{R}^d$ is perturbed, $h$ changes as $\Delta h = W_e \delta$; due to non-orthogonality ($v_i \cdot v_j \neq 0$), one feature's activation can generate “ghost” activations in others—this is feature interference.

AIS formalizes the optimal adversarial perturbation $\delta^*$ as $\delta^* \propto W_e^\top (v_k - v_j)$ under an $\ell_2$ budget, creating predictable attack directions. Attack success, transferability, and class-wise vulnerabilities arise as functions of input feature compressibility, non-orthogonal latent geometry, and correlation-induced constraints.

2. AIS Methodologies Across Application Domains

AIS methodology adapts to the structure of the underlying system:

• Neural networks: Create adversarial input perturbations exploiting representational bottlenecks and superposed directions, validated by latent alignment measurements and transfer experiments (Stevinson et al., 13 Oct 2025).
• Radar countermeasures: AIS pipelines translate image-domain attacks (e.g., DITIMI-FGSM on spectrograms) into physically realizable time-domain jamming waveforms via STFT inversion—enabling imperceptible yet process-targeted electronic countermeasures (Ma et al., 2023).
• Secure ISAC (Integrated Sensing and Communication): Artificial ambiguity function (AF) engineering with structured OFDM subcarrier power allocation superimposes fake targets for unauthorized receivers, while mismatched filtering at the legitimate party suppresses artifacts at controlled SNR loss (Han et al., 2 Oct 2025).
• Intelligent Surface (IS) radar stealth: IS phase profile is optimized (minimax game) to maximize sensing estimation distortion while meeting communication SNR constraints. Closed-form geometric projections yield per-element phase solutions and quantify AoA error (Xu et al., 26 Jan 2025).
• Multi-layer EW/cyber/deception for autonomous control: Simulations integrate electronic jamming, cyber intrusion (data integrity distortion), and active decoys to degrade missile guidance laws. Deep reinforcement learning (PPO) coordinates actions for maximal disruption under resource constraints (Alimoradi et al., 3 Oct 2025).
• Adversarial board game attacks: Minimal, semantically invariant state perturbations (e.g., meaningless moves in Go) reliably induce suboptimal neural policy/value behavior using formal examiner-based criteria and combinatorial search-space reductions (Lan et al., 2022).
• Wireless multi-agent learning: Zero-sum adversarial RL games simulate worst-case interference by co-training aggressive and defensive agents. History-informed state representations and reward structure ensure robustness against unpredictable, uncoordinated APs (Kihira et al., 2020).

3. Synthetic and Real-World AIS Experiments

AIS efficacy is validated in both synthetic and operational environments:

• Superposition-driven attacks: Synthetic settings with controlled $k/m$ ratios show robustness decay with increased superposition pressure; PGD perturbations align with theoretical $\delta^*$ at cosine similarities $>0.9$ for wide regimes (Stevinson et al., 13 Oct 2025).
• Transferability: Adversarial perturbations generated in one architecture (e.g., neural models, radar classifiers) transfer reliably to structurally similar models; attack transfer rates scale with latent representation geometric similarity and input correlation (Stevinson et al., 13 Oct 2025, Ma et al., 2023).
• Detection, estimation, and jamming: In OFDM ISAC, artificial target peaks insert ambiguity for unauthorized eavesdroppers while maintaining legitimate estimation performance using mismatched filters; Eve’s RMSE balloons by orders of magnitude under security constraints (Han et al., 2 Oct 2025).
• Physical countermeasures: Radar shielding via IS phase control distorts unauthorized angle estimates by up to $30\%$ more than baseline methods, holding communication SNR loss to $<0.5$ dB (Xu et al., 26 Jan 2025). Multi-layer missile interference increases angular deviation $>3300\%$ and drops success rate from $92.7\%$ to $31.5\%$ (Alimoradi et al., 3 Oct 2025).

Domain	AIS Mechanism	Key Quantitative Effect
Neural nets	Latent feature interference	PGD–theory cosine $\sim$0.97; robust acc. $\downarrow$ as $k/m\uparrow$
Radar images	TF-image STFT inversion	Black-box transfer $>60\%$; imperceptible time-domain jamming
ISAC	AF engineering, power alloc	Eve RMSE 100$\times$, SNR loss Alice $<8$ dB
IS stealth	IS phase minimax	AoA error $30\%$ vs. baseline, SNR loss $<0.5$ dB
Missile EW/CI	Multi-layer RL coordination	Deviation $8.65^\circ$ (vs. $0.25^\circ$ baseline), success rate $31.5\%$
Board games	Semantic pert, search prune	Fool PV-NN $>90\%$ with $2$ moves; transfer to NoGo $50\%$
Wireless RL	Adversarial zero-sum games	Throughput up to $1.6$ Mbit/slot; min throughput increase $>30\%$

4. Formal Algorithms and Simulation Recipes

AIS simulation frameworks feature explicit pseudocode and optimization procedures:

• Gradient-based adversarial generation: Projected gradient descent algorithms, theoretical alignment steps, and budgeted perturbation computation are consistently used in neural and radar image domains (Stevinson et al., 13 Oct 2025, Ma et al., 2023).
• Feature extraction: Sparse autoencoder probes, linear regression from activations to labels, and OFDM subcarrier power design (comb/periodic allocations) are standard for feature interference/macroscopic ambiguity injection (Stevinson et al., 13 Oct 2025, Han et al., 2 Oct 2025).
• Game-theoretic, convex, and projected solutions: IS phase optimization (complex-plane minimax projection), power allocation convex programming (fractional-linear, bisection), and multi-agent RL reward design underpin physical stealth, energy-efficient jamming, and wireless coordination (Xu et al., 26 Jan 2025, Wang et al., 22 Dec 2025, Alimoradi et al., 3 Oct 2025, Kihira et al., 2020).
• Empirical evaluation metrics: Cosine similarity, attack success/transfer percent, SNR loss, root-MUSIC RMSE, angular deviation, throughput, and collision probability are standard.

5. Impact: Transferability, Robustness, and Defense Implications

Adversarial interference, once viewed as a consequence of idiosyncratic model error or non-robust inputs, is shown to originate from system-intrinsic feature packing, compression, and geometric arrangements. Empirical and theoretical analyses support predictability of attack patterns, high transfer rates between models with shared geometry (up to $94\%$ in synthetic tests), and explain class-wise vulnerability phenomena (Stevinson et al., 13 Oct 2025). Multi-layer simulation exposes synergistic effects (EW + cyber + deception); composite strategies yield superadditive performance degradation against autonomous guidance and sensor fusion (Alimoradi et al., 3 Oct 2025).

AIS also guides defense and system design:

• Geometry decorrelation (data or architecture) reduces interference pathways.
• Latent bottlenecking is a vulnerability amplifier; increasing latent dimensionality or imposing orthogonality decreases susceptibility.
• Defensive RL frameworks gain robustness only when adversarial patterns are actively simulated during training (Kihira et al., 2020).
• Cognitive jamming and adaptive power allocation create sustained disruption under resource constraints (Wang et al., 22 Dec 2025).

6. Extensions and Generalization

AIS frameworks are generically extensible:

• Other sensing modalities: Doppler/range estimation, through redefinition of the utility functions and metric projections (Xu et al., 26 Jan 2025, Wang et al., 22 Dec 2025).
• Continuous state/control: Perturbation sets and examiner-based evaluations generalize to RL, vision, and control benchmarks (Lan et al., 2022).
• Multi-agent and distributed systems: Modeling unknown interferers as adversaries, deploying layered simulation with context-aware resource allocation, and using alternating-projection/robust optimization are readily portable (Kihira et al., 2020, Alimoradi et al., 3 Oct 2025).
• Machine-learning driven jamming and stealth: Data-driven solvers replace analytic fractional programming under realistic CSI assumptions (Wang et al., 22 Dec 2025).

AIS thus constitutes a comprehensive paradigm for adversarial vulnerability analysis, simulation-based defense design, and predictable transferability modeling across neural, sensor, and control systems. Its key insight is that interference, when combined with embedded system constraints, produces mechanistically interpretable and attackable pathways—offering both diagnostic and proactive guidance for secure system engineering.