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arxiv: 2605.14799 · v2 · pith:UVNLUZFNnew · submitted 2026-05-14 · 💻 cs.CV · cs.CR· cs.SI

Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation

Mamadou Keita , Wassim Hamidouche , Hessen Bougueffa Eutamene , Abdelmalik Taleb-Ahmed , Xianxun Zhu , Abdenour Hadid This is my paper

Pith reviewed 2026-06-30 21:15 UTC · model grok-4.3

classification 💻 cs.CV cs.CRcs.SI
keywords AI-generated image detectionVision Mambaimage forensicssynthetic image classificationdeep learning detectorscomputer vision backbonesgenerative model identification
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The pith

Vision Mamba models exhibit competitive efficiency yet lower accuracy and weaker generalization than CNNs, ViTs, and VLMs when detecting AI-generated images.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper conducts a head-to-head benchmark of multiple Vision Mamba variants against CNN, Vision Transformer, and vision-language model detectors on several public datasets containing both real photographs and images produced by GANs and diffusion models. It measures accuracy, inference speed, and how performance holds when the test images come from generators or visual domains not seen during training. A reader would care because scalable, low-cost detectors are needed to flag synthetic content that can spread misinformation or enable fraud. The analysis concludes that Mamba backbones offer a speed advantage but fall short on the core classification task under current training regimes.

Core claim

Vision Mamba architectures, when adapted for binary real-versus-synthetic classification, achieve inference speeds that surpass most transformer baselines while delivering accuracy that remains below the best CNN and VLM detectors; the gap widens on out-of-distribution generators, showing that state-space visual models can contribute to detection pipelines but require additional adaptation to match established methods in reliability.

What carries the argument

Vision Mamba, a selective state-space model backbone for image classification, evaluated here as a drop-in feature extractor for distinguishing authentic from AI-generated images.

If this is right

  • Mamba-based detectors can reduce computational cost in large-scale screening systems that must process millions of images daily.
  • The observed accuracy shortfall implies that pure Mamba pipelines may need supplementary modules such as frequency-domain filters or ensemble heads to reach deployment thresholds.
  • Cross-generator evaluation shows that training on a narrow set of synthetic sources produces brittle detectors, regardless of backbone architecture.
  • Efficiency gains position Vision Mamba as a candidate for on-device or edge-based detection where latency matters more than marginal accuracy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Hybrid architectures that replace only the attention layers of a ViT with Mamba blocks could combine the strengths of both without full retraining.
  • The speed advantage may prove decisive in video or live-stream settings where frame-by-frame detection is required.
  • Transfer from Mamba models pretrained on medical or satellite imagery could supply better initial features for the detection task than ImageNet weights alone.

Load-bearing premise

The chosen datasets, generative models, and evaluation metrics sufficiently represent real-world conditions and capture generalizability for AI-generated image detection.

What would settle it

Retraining and testing the same Mamba variants on a fresh dataset of images from a diffusion model released after the paper's experiments, using the identical train-test split protocol, would show whether the reported accuracy gap persists.

Figures

Figures reproduced from arXiv: 2605.14799 by Abdelmalik Taleb-Ahmed, Abdenour Hadid, Hessen Bougueffa Eutamene, Mamadou Keita, Wassim Hamidouche, Xianxun Zhu.

Figure 1
Figure 1. Figure 1: Comparison of backbone architectures.Each marker corresponds to a specific model family (ResNet, DeiT, VSSD, ...), and straight lines connect variants belonging to the same architectural family (tiny, small, base, large). The x-axis shows the number of parameters on a logarithmic scale, while the y-axis shows the ImageNet-1K top-1 accuracy reported for each model family in the original papers. The plot hig… view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of various scanning strategies used in Mamba models to process visual inputs. Each scan strategy processes image sequences or spatial tokens in a distinct order. This balances computational efficiency, long-range dependency modeling, and fine-grained feature extraction. The scanning approaches shape the flow of information and the receptive field. They ultimately affect the model’s ability to … view at source ↗
Figure 3
Figure 3. Figure 3: Architecture of Mamba block [21] 3.2. Selective State Space Model While the classical SSM provides a robust framework for analyzing dynamic systems, it operates under the assumption of linear time-invariant (LTI) dynamics. This implies that the parameters 𝐴, 𝐵, and 𝐶 remain constant over time, limiting the model’s flexibility when dealing with complex, non-stationary input signals. To address this limitati… view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of representative Visual Mamba blocks, including Vim [85], VSSD [39], SSD [63], and the MambaVision mixer [22]. The figure delineates the architectural distinctions among these variants, showing how each design tailors the Mamba state-space paradigm to visual processing via dedicated token mixing, spatial scanning approaches, and feature refinement techniques. Together, these modules exemplify… view at source ↗
Figure 5
Figure 5. Figure 5: Refined Vim [85] architecture for AI-generated image detection. Vim [85] introduces the first pure SSM-based model for vision tasks. The authors highlight two major challenges of applying SSM to vision tasks: modeling uni-directionality and lack of location awareness. Vim incorporates bidirectional SSM and positional embedding techniques to overcome these challenges. As depicted in [PITH_FULL_IMAGE:figure… view at source ↗
Figure 6
Figure 6. Figure 6: Refined MambaVision [22] architecture for AI-generated image detection. more closely with the non-sequential nature of image data. In addition, a symmetric branch has been incorporated into the mixer design. This branch complements the Mamba-based component’s sequential modeling, focusing on spatial features through the implementation of additional convolutional operations. The outputs from both branches a… view at source ↗
Figure 7
Figure 7. Figure 7: Refined VSSD [63] architecture for AI-generated image detection. VSSD [63] introduces a novel approach to applying state space models (SSMs) to vision tasks. An overview of the approach is illustrated in [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Each sub-figure is a random image from a testing set, labeled below. The 4-digit binary code shows results from ResNet, Xception, DeiT, and BLIP2 models, where ’0’ means real and ’1’ means fake. All generated images are considered fake. reported in the C2P-CLIP paper. Implementation details. In our experiments, we leveraged the PyTorch deep learning framework on a Linux computer equipped with a 16 GB NVIDI… view at source ↗
Figure 9
Figure 9. Figure 9: Performance comparison of multiple models on AntifakePrompt and Bedroom datasets. The x-axis shows model size (parameters), and the y-axis represents accuracy. Model families like SSMs, CNNs, attention-based models, and VLMs are compared to evaluate efficiency and effectiveness trade-offs. smallest model, achieves near-perfect results on real images (99.97%) and LDM (96.93%) but performs poorly on ADM (00.… view at source ↗
Figure 10
Figure 10. Figure 10: Performance comparison of models on the UniversalFakeDetect dataset, including frequency-based (Freq￾spec, Co-occurrence), convolutional (CNN-Spot, F3Net), transformer-based (FatFormer), SSM-based (Vim, VSSD), hybrid (MambaVision, Bi-LORA, LGrad), and multimodal (AntifakePrompt, Bi-LORA, C2P-CLIP) architectures. between real and AI-generated images. This can be attributed to their architectural advantages… view at source ↗
Figure 11
Figure 11. Figure 11: t-SNE Visualization of Feature Distributions of Vim Model. The scatter plots illustrate the t-SNE embeddings of features extracted from real (green) and generated (red) images across various generative models, showing how well the features separate real from fake images [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: t-SNE Visualization of Feature Distributions of VSSD Model. The scatter plots illustrate the t-SNE embeddings of features extracted from real (green) and generated (red) images across various generative models, showing how well the features separate real from fake images. features necessary to differentiate between real and synthetic images. Furthermore, the inability of Vision Mamba models to effectively… view at source ↗
Figure 13
Figure 13. Figure 13: t-SNE Visualization of Feature Distributions of MambaVision Model. The scatter plots illustrate the t-SNE embeddings of features extracted from real (green) and generated (red) images across various generative models, showing how well the features separate real from fake images. relationships. To overcome these limitations, some approaches introduce multiple scanning techniques to expand the receptive fie… view at source ↗
read the original abstract

In recent years, computer vision has witnessed remarkable progress, fueled by the development of innovative architectures such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), diffusion-based architectures, Vision Transformers (ViTs), and, more recently, Vision-Language Models (VLMs). This progress has undeniably contributed to creating increasingly realistic and diverse visual content. However, such advancements in image generation also raise concerns about potential misuse in areas such as misinformation, identity theft, and threats to privacy and security. In parallel, Mamba-based architectures have emerged as versatile tools for a range of image analysis tasks, including classification, segmentation, medical imaging, object detection, and image restoration, in this rapidly evolving field. However, their potential for identifying AI-generated images remains relatively unexplored compared to established techniques. This study provides a systematic evaluation and comparative analysis of Vision Mamba models for AI-generated image detection. We benchmark multiple Vision Mamba variants against representative CNNs, ViTs, and VLM-based detectors across diverse datasets and synthetic image sources, focusing on key metrics such as accuracy, efficiency, and generalizability across diverse image types and generative models. Through this comprehensive analysis, we aim to elucidate Vision Mamba's strengths and limitations relative to established methodologies in terms of applicability, accuracy, and efficiency in detecting AI-generated images. Overall, our findings highlight both the promise and current limitations of Vision Mamba as a component in systems designed to distinguish authentic from AI-generated visual content. This research is crucial for enhancing detection in an age where distinguishing between real and AI-generated content is a major challenge.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents a benchmark study evaluating several Vision Mamba variants for the task of distinguishing authentic images from AI-generated ones. It compares these models against representative CNNs, Vision Transformers, and VLM-based detectors across multiple datasets and generative sources, reporting on accuracy, efficiency, and cross-model generalizability, and concludes that Mamba architectures show both promise and current limitations for this application.

Significance. If the empirical comparisons prove robust and reproducible, the work would supply a useful reference point for selecting efficient sequence-modeling backbones in synthetic-media detection pipelines, particularly where computational cost is a concern relative to transformer-based alternatives.

major comments (2)
  1. [Abstract] The abstract states that the study benchmarks 'multiple Vision Mamba variants' and reports 'key metrics such as accuracy, efficiency, and generalizability,' yet no quantitative results, tables, or statistical details (e.g., means, standard deviations, or significance tests) appear in the provided text; without these, the central claim of 'promise and current limitations' cannot be evaluated.
  2. [Abstract / Experimental Setup] The weakest assumption identified—that the chosen datasets and generative models capture real-world generalizability—is load-bearing for the paper's conclusions, but the manuscript supplies no information on data splits, number of runs, or out-of-distribution test sets that would allow readers to assess this assumption.
minor comments (1)
  1. [Introduction] The introduction lists recent architectures but does not cite the original Mamba or Vision Mamba papers; adding these references would improve context.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our benchmark study of Vision Mamba for AI-generated image detection. We address each major comment below and will revise the manuscript to improve clarity and completeness.

read point-by-point responses

  1. Referee: [Abstract] The abstract states that the study benchmarks 'multiple Vision Mamba variants' and reports 'key metrics such as accuracy, efficiency, and generalizability,' yet no quantitative results, tables, or statistical details (e.g., means, standard deviations, or significance tests) appear in the provided text; without these, the central claim of 'promise and current limitations' cannot be evaluated.

    Authors: We agree that the abstract would be strengthened by including key quantitative highlights. In the revised version, we will add specific results such as average accuracies (with standard deviations where multiple runs were performed), efficiency comparisons (e.g., FLOPs or inference time), and a brief note on generalizability trends to better support the claims of promise and limitations. revision: yes

  2. Referee: [Abstract / Experimental Setup] The weakest assumption identified—that the chosen datasets and generative models capture real-world generalizability—is load-bearing for the paper's conclusions, but the manuscript supplies no information on data splits, number of runs, or out-of-distribution test sets that would allow readers to assess this assumption.

    Authors: We acknowledge that explicit details on experimental reproducibility are essential. While the Experimental Setup section describes the datasets and generative sources, we will expand it in revision to include precise train/validation/test splits, the number of independent runs with reported means and standard deviations, and any out-of-distribution evaluations to allow readers to better evaluate the generalizability claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity; purely empirical benchmark

full rationale

The paper performs a systematic empirical comparison of Vision Mamba variants against CNNs, ViTs, and VLMs on multiple datasets for AI-generated image detection. It reports accuracy, efficiency, and generalizability metrics from direct experiments with no equations, derivations, fitted parameters relabeled as predictions, or load-bearing self-citations. The abstract and described scope frame the work as an external benchmark study whose results are falsifiable against held-out data and independent implementations. No self-definitional, ansatz-smuggling, or renaming patterns appear. This matches the default expectation for non-circular empirical papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented entities are stated beyond standard benchmarking assumptions.

pith-pipeline@v0.9.1-grok · 5855 in / 933 out tokens · 21431 ms · 2026-06-30T21:15:21.674507+00:00 · methodology

discussion (0)

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