Across cross-generator, post-processing, and in-the-wild benchmarks, PE-SPC surpasses the previous DINOv3 baseline and achieves new state-of-the-art results.
Abstract
Recent work has shown that a simple linear probe on frozen representations from modern vision foundation models (VFMs) can achieve state-of-the-art AIGI detection performance, substantially outperforming specialized detectors in challenging in-the-wild scenarios. This finding has established DINOv3 as the dominant foundation-model baseline for subsequent improvements. However, we find that the vision-language model Perception Encoder (PE) holds greater potential for AIGI detection, because its language-aligned representation preserves high-level provenance semantics. Specifically, PE exhibits stronger local provenance organization than DINOv3 in its frozen feature space. However, semantic-agnostic linear probing fails to exploit this structure, as PE-Linear still underperforms DINOv3-Linear by 4.1% on In-the-Wild. Based on this observation, we propose Semantic Prototype Calibration (SPC), which constructs category prototypes from forensic semantic information and calibrates them with supervised data. We apply SPC to PE and refer to the resulting detector as PE-SPC. Our analysis shows that this simple design achieves stronger generalization. Across cross-generator, post-processing, and in-the-wild benchmarks, PE-SPC surpasses the previous DINOv3 baseline and achieves new state-of-the-art results.
The Robust and Fine-grained training framework for CLIP-based vision-language models (RoFLIP) is proposed, enhancing both the robustness and granularity of vision-language alignment and underscore RoFLIP’s compositional reasoning and generalization abilities.
Yiwei Sun, Chuanbin Liu, Shancheng Fang et al.· International Journal of Com...· 0 citations
Transfer learning with ImageNet-pretrained backbones is ubiquitous in computer vision, yet the question of which intermediate layer best serves a downstream task is typically resolved by convention rather than controlled measurement. Despite the prevalence of this practice, no systematic comparison of layer-wise frozen representations has been conducted across the modern architectures that dominate current pipelines. We present the Layer Transferability Profile, a reproducible evaluation protocol and an accompanying metric, the Layer-Task Transferability Score (LTTS), that together quantify how transferable information is distributed across depth. Applying the profile to three architectures, ResNet-50, EfficientNet-B0, and ViT-B/16, on three recognition benchmarks (CIFAR-10, CIFAR-100, and Food101), we find that transfer performance improves monotonically with depth and that the final or near-final representation is consistently optimal (LTTS 0-1.2%). This pattern contrasts with recent findings in speech and language foundation models, where intermediate layers can yield substantial gains, suggesting that the concentration of transferability in late layers is characteristic of supervised visual pretraining for recognition tasks rather than a universal property of deep representations. Notably, ViT-B/16 achieves the strongest transfer accuracy (78.1% on CIFAR-100, 94.6% on CIFAR-10, and 68.6% on Food-101) while using 768-dimensional features, outperforming ResNet-50’s 2048-dimensional and EfficientNet-B0’s 1280-dimensional representations. This indicates that representational quality per dimension varies substantially across architectures and can be more important than raw feature size when selecting a backbone for transfer.
Sanghyuk Roy Choi, Chanhoe Gu, S. Kim et al.· International Conference on...· 0 citations
The recent progress in the vision-language model (VLM) research made it one of the key aspects of artificial intelligence due to joint vision and language processing capabilities required for such tasks as image retrieval, visual question answering, autonomous systems and medical image analysis. However, despite the excellent results obtained with such systems, they still appear to be extremely vulnerable to adversarial perturbations which result in a significant decline in prediction accuracy and reliability due to even subtle input transformations. The purpose of this paper is to provide a systematic method for analyzing the adversarial robustness of vision-language models under both clean and perturbed conditions. The suggested framework is based on Semantic Counterfactual Augmentation (SCA) and Curriculum Contrastive Adversarial Training (CCAT) and aims to maintain semantic consistency and increase the robustness of the model respectively. The framework is tested using a CLIP-based vision-language model on the subset of Flickr8k dataset with Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks. As a result of the experiment, the performance gap is identified between the clean and perturbed images which confirms the vulnerabilities of the conventional VLM while indicating better robustness consistency with the suggested framework.
Kumari Anjali, Karnatakam Veda Sahithi, Sakinala Jyotsna· 2026 4th International Confe...· 0 citations
A data-generation pipeline that captions real photographs with a vision–language model and regenerates them with modern text-to-image systems, producing semantically aligned real/synthetic pairs that isolate generative artifacts from image content is described.
Vision-Language Large Models (VLLMs) are promising for AI-generated image (AIGI) detection because they can produce both a prediction and a natural-language output. However, most existing VLLM-based detectors primarily fine-tune the language side while giving limited attention to low-level visual forensic cues. They also often depend on manually crafted prompts or human-annotated rationales, which limits scalability.We present UC-VLM, a unified multi-stage framework for AIGI detection that relies solely on binary supervision. UC-VLM first identifies effective instruction variants automatically. It then reuses the same binary label within a multi-stage training framework: (i) a visual discrimination objective that strengthens sensitivity to non-semantic forensic cues, and (ii) a label-conditioned generation objective that uses the binary label to supervise textual outputs. This design turns weak binary supervision into a shared supervision signal for both the visual pathway and the language output. Our key novelty is a unified multi-stage binary-supervised framework that consistently reuses the same authenticity labels for visual adaptation and label-conditioned text generation, while leveraging automatically optimized instructions to reduce prompt sensitivity without requiring human-written rationales or hand-crafted prompts.Experiments show that UC-VLM achieves 96.1% average accuracy on GenImage, exceeding the strongest prior result by 4.6%, and obtains 69.6% / 77.9% accuracy on Chameleon under ProGAN / SDV1.4 training, surpassing the best baseline by 11.2% / 15.3%, respectively.
Lei Tan, Shuwei Li, Mohan S. Kankanhalli et al.· 0 citations
Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text retrieval, showing that feature-level distillation strengthens both compositional and general vision-language representation capability.
Jongsuk Kim, Qiyu Wu, Zhuoyuan Mao et al.· 0 citations
