A Multi-View and Confusion-Guided Ensemble Framework for Robust Synthetic Image Attribution

• URL: http://arxiv.org/abs/2609.11188v1
• Date: Thu, 10 Sep 2026 07:54:25 GMT
• Title: A Multi-View and Confusion-Guided Ensemble Framework for Robust Synthetic Image Attribution
• Abstract summary: We present a multi-view and confusion-guided ensemble framework for the Synthetic Image Attribution Challenge of the DLMMDD Workshop at ICANN 2026.<n>Our approach integrates multiple complementary architectures, including FFT-ConvNeXt, DINOv2, CLIP, and Xception, to capture diverse attribution cues from frequency, semantic, and forensic perspectives.<n>We analyze the confusion patterns of the ensemble model and observe severe ambiguity between Stable Diffusion 3 and Stable Diffusion 3.5.
• Score: 0.6768558752130311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic image attribution (SIA) has become increasingly important with the rapid advancement of text-to-image generation models. However, accurately identifying the source model of a generated image remains challenging due to the growing similarity among modern diffusion-based generators and the presence of diverse post-processing operations. In this report, we present a multi-view and confusion-guided ensemble framework for the Synthetic Image Attribution Challenge of the DLMMDD Workshop at ICANN 2026. Our approach integrates multiple complementary architectures, including FFT-ConvNeXt, DINOv2, CLIP, and Xception, to capture diverse attribution cues from frequency, semantic, and forensic perspectives. To improve robustness against unknown degradations and image manipulations, extensive data augmentation strategies are employed during training, simulating realistic post-processing operations such as compression, resizing, grayscale conversion, and blur. Furthermore, we analyze the confusion patterns of the ensemble model and observe severe ambiguity between Stable Diffusion 3 and Stable Diffusion 3.5. To address this issue, we introduce a dedicated binary expert classifier that is selectively activated under low-confidence conditions. We additionally apply class-adaptive confidence calibration to improve the discrimination of challenging classes such as Tencent Hunyuan. The proposed framework achieved 99.53% on the public leaderboard and 99.20% on the private leaderboard. The source code and implementation details are publicly available at https://github.com/ZOMIN28/SIA.

Related papers

• HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval [32.85064790572752]
  Composed Image Retrieval (CIR) is a flexible image retrieval paradigm that enables users to accurately locate the target image.<n>This paper proposes a cHrono-synergiA roBust progressIve learning framework for composed image reTrieval (HABIT)<n>Experiments conducted on two standard CIR datasets demonstrate that HABIT significantly outperforms most methods under various noise ratios.
  arXiv  Detail & Related papers  (2026-04-20T10:02:18Z)
• Group Diffusion: Enhancing Image Generation by Unlocking Cross-Sample Collaboration [88.94434023253872]
  We propose Group Diffusion, unlocking the attention mechanism to be shared across images.<n>We observe a clear scaling effect - larger group sizes yield stronger cross-sample attention and better generation quality.<n>Our work reveals cross-sample inference as an effective, previously unexplored mechanism for generative modeling.
  arXiv  Detail & Related papers  (2025-12-11T18:59:55Z)
• Towards Unified Semantic and Controllable Image Fusion: A Diffusion Transformer Approach [99.80480649258557]
  DiTFuse is an instruction-driven framework that performs semantics-aware fusion within a single model.<n>Experiments on public IVIF, MFF, and MEF benchmarks confirm superior quantitative and qualitative performance, sharper textures, and better semantic retention.
  arXiv  Detail & Related papers  (2025-12-08T05:04:54Z)
• SDiFL: Stable Diffusion-Driven Framework for Image Forgery Localization [46.258797633731746]
  Existing image forgery localization methods rely on labor-intensive and costly annotated data.<n>We are the first to integrate both image generation and powerful perceptual capabilities of SD into an image forensic framework.<n>Our framework achieves up to 12% improvements in performance on widely used benchmarking datasets.
  arXiv  Detail & Related papers  (2025-08-27T18:02:09Z)
• MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling [64.09238330331195]
  We propose a novel Multi-Modal Auto-Regressive (MMAR) probabilistic modeling framework.<n>Unlike discretization line of method, MMAR takes in continuous-valued image tokens to avoid information loss in an efficient way.<n>We also propose a theoretically proven technique that addresses the numerical stability issue and a training strategy that balances the generation and understanding task goals.
  arXiv  Detail & Related papers  (2024-10-14T17:57:18Z)
• Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model [80.61157097223058]
  A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models. In this study, we scrutinize the shortcomings of both current generative and conventional data augmentation techniques. We introduce an innovative inter-class data augmentation method known as Diff-Mix, which enriches the dataset by performing image translations between classes.
  arXiv  Detail & Related papers  (2024-03-28T17:23:45Z)
• Improving Few-shot Image Generation by Structural Discrimination and Textural Modulation [10.389698647141296]
  Few-shot image generation aims to produce plausible and diverse images for one category given a few images from this category. Existing approaches either globally interpolate different images or fuse local representations with pre-defined coefficients. This paper proposes a novel mechanism to inject external semantic signals into internal local representations.
  arXiv  Detail & Related papers  (2023-08-30T16:10:21Z)
• DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability [75.9781362556431]
  We propose DiffDis to unify the cross-modal generative and discriminative pretraining into one single framework under the diffusion process. We show that DiffDis outperforms single-task models on both the image generation and the image-text discriminative tasks.
  arXiv  Detail & Related papers  (2023-08-18T05:03:48Z)
• Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion [22.237426507711362]
  Model-Agnostic Zero-Shot Classification (MA-ZSC) refers to training non-specific classification architectures to classify real images without using any real images during training. Recent research has demonstrated that generating synthetic training images using diffusion models provides a potential solution to address MA-ZSC. We propose modifications to the text-to-image generation process using a pre-trained diffusion model to enhance diversity.
  arXiv  Detail & Related papers  (2023-02-07T07:13:53Z)
• Auto-regressive Image Synthesis with Integrated Quantization [55.51231796778219]
  This paper presents a versatile framework for conditional image generation. It incorporates the inductive bias of CNNs and powerful sequence modeling of auto-regression. Our method achieves superior diverse image generation performance as compared with the state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-21T22:19:17Z)
• Encoding Robustness to Image Style via Adversarial Feature Perturbations [72.81911076841408]
  We adapt adversarial training by directly perturbing feature statistics, rather than image pixels, to produce robust models. Our proposed method, Adversarial Batch Normalization (AdvBN), is a single network layer that generates worst-case feature perturbations during training.
  arXiv  Detail & Related papers  (2020-09-18T17:52:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.