On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations | Paper Citation Record

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This paper cites Generative imperceptible attack with feature learning bias reduction and multi-scale variance reg- ularization,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Generative imperceptible attack with feature learning bias reduction and multi-scale variance reg- ularization,

Reference 1

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Semantically consistent visual representation for adversarial robustness,

Reference 2

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations B-avibench: Toward evaluating the robustness of large vision-language model on black-box adversarial visual-instructions,

Reference 3

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This paper cites Vision-language models for vision tasks: A survey,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Vision-language models for vision tasks: A survey,

Reference 4

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This paper cites Towards multimodal disinformation detection by vision-language knowledge in- teraction,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Towards multimodal disinformation detection by vision-language knowledge in- teraction,

Reference 5

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This paper cites Media forensics and deepfakes: An overview,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Media forensics and deepfakes: An overview,

Reference 6

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This paper cites Plausible may not be faithful: Probing object hallucination in vision-language pre-training,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Plausible may not be faithful: Probing object hallucination in vision-language pre-training,

Reference 7

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This paper cites Application of fourier analysis to the visibility of gratings,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Application of fourier analysis to the visibility of gratings,

Reference 8

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Unresolved cited work

Reference 9

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This paper cites Drop an octave: Reducing spatial redundancy in convo- lutional neural networks with octave convolution,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Drop an octave: Reducing spatial redundancy in convo- lutional neural networks with octave convolution,

Reference 10

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This paper cites Spatial frequency enhanced salient object detection,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Spatial frequency enhanced salient object detection,

Reference 11

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This paper cites Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions,

Reference 12

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This paper cites Adversarial examples are not bugs, they are features,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Adversarial examples are not bugs, they are features,

Reference 13

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This paper cites A fourier perspective of feature extraction and adversarial robustness,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations A fourier perspective of feature extraction and adversarial robustness,

Reference 14

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This paper cites Efficient generation of targeted and transferable adversarial examples for vision-language mod- els via diffusion models,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Efficient generation of targeted and transferable adversarial examples for vision-language mod- els via diffusion models,

Reference 15

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This paper cites Overload: Latency attacks on object detection for edge devices,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Overload: Latency attacks on object detection for edge devices,

Reference 16

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This paper cites Survivability analysis of iot systems under resource exhausting attacks,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Survivability analysis of iot systems under resource exhausting attacks,

Reference 17

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This paper cites Learning transferable visual models from natural language supervi- sion,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Learning transferable visual models from natural language supervi- sion,

Reference 18

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Rgfreq dataset,

Reference 19

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Generative adversarial nets,

Reference 20

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This paper cites Auto-Encoding Variational Bayes,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Auto-Encoding Variational Bayes,

Reference 21

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Denoising diffusion probabilistic models,

Reference 22

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Denoising diffusion implicit models,

Reference 23

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 24

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 25

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 26

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations U-net: Convolutional networks for biomedical image segmentation,

Reference 27

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations High- resolution image synthesis with latent diffusion models,

Reference 28

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 29

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 30

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Deep residual learning for image recognition,

Reference 31

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations An image is worth 16x16 words: Trans- formers for image recognition at scale,

Reference 32

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations VQA: Visual question answering,

Reference 33

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 34

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations On the Opportunities and Risks of Foundation Models

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Stacked cross attention for image-text matching,

Reference 37

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Flow straight and fast: Learning to gen- erate and transfer data with rectified flow,

Reference 38

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This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 39

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations PaliGemma: A versatile 3B VLM for transfer

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Learning Rich Features for Image Manipulation Detection,

Reference 42

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On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations FaceForensics++: Learning to Detect Manipulated Facial Images,

Reference 43

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This paper cites The DeepFake Detection Challenge (DFDC) Dataset.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations The DeepFake Detection Challenge (DFDC) Dataset

Reference 44

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This paper cites Detecting images generated by diffusers,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Detecting images generated by diffusers,

Reference 45

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This paper cites DIRE: Diffusion Reconstruction Error for Diffusion-Generated Image Detection,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations DIRE: Diffusion Reconstruction Error for Diffusion-Generated Image Detection,

Reference 46

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This paper cites Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024

Reference 47

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This paper cites Synth- Buster: Towards Detection of Diffusion Model Generated Images,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Synth- Buster: Towards Detection of Diffusion Model Generated Images,

Reference 48

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This paper cites Llms are not yet ready for deepfake image detection,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Llms are not yet ready for deepfake image detection,

Reference 49

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This paper cites Intriguing properties of neural networks,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Intriguing properties of neural networks,

Reference 50

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This paper cites Explaining and harnessing adversarial examples,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Explaining and harnessing adversarial examples,

Reference 51

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This paper cites Towards deep learning models resistant to adversarial attacks,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Towards deep learning models resistant to adversarial attacks,

Reference 52

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Observation ede04c7d-91bd-478b-8bea-cbdb0fac0d2b · outbound

This paper cites Adversarial vqa: A new benchmark for evaluating the robustness of vqa models,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Adversarial vqa: A new benchmark for evaluating the robustness of vqa models,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:05.247004Z

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Observation eab56efe-9611-4166-a34c-2b7548b0e96e · outbound

This paper cites Attacking vqa systems via adversarial background noise,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Attacking vqa systems via adversarial background noise,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:05.069052Z

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Observation 8213d409-3c2a-4eb3-b7bb-863c849af378 · outbound

This paper cites On evaluating adversarial robustness of large vision-language models,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations On evaluating adversarial robustness of large vision-language models,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:04.916129Z

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Observation 2bc95700-2598-4af3-b982-db7496d87ff4 · outbound

This paper cites Mutual-modality adversarial attack with semantic perturbation,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Mutual-modality adversarial attack with semantic perturbation,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:04.797875Z

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Observation 4d040643-f66b-40e6-8299-4abb08bc0f2a · outbound

This paper cites Frequency-driven imperceptible adversarial attack on semantic similarity,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Frequency-driven imperceptible adversarial attack on semantic similarity,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:04.606945Z

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Observation d23d115c-d627-4fd2-9dd9-0b062b20e408 · outbound

This paper cites Facl-attack: Frequency-aware contrastive learning for transferable adversarial attacks,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Facl-attack: Frequency-aware contrastive learning for transferable adversarial attacks,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:04.398946Z

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Observation 6fd93d75-641a-4037-86a5-ef45924d9ebb · outbound

This paper cites AdvDiff: Generating unrestricted adversarial examples using diffusion models,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations AdvDiff: Generating unrestricted adversarial examples using diffusion models,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:04.134539Z

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Observation 48bef1b1-7e8e-4194-a705-2caf1677909d · outbound

This paper cites Sita: Structurally imperceptible and transferable adversarial attacks for stylized image generation,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Sita: Structurally imperceptible and transferable adversarial attacks for stylized image generation,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:03.905311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8da632b0-7948-4b03-abd1-5ddc62ed65c0 · outbound

This paper cites Toward transferable attack via adver- sarial diffusion in face recognition,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Toward transferable attack via adver- sarial diffusion in face recognition,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:03.699649Z

Source-reported events for the cited work

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Observation 4e2e9ca7-a281-4788-a8fa-6d7acdd0246c · outbound

This paper cites BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:03.458002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 78db761b-45a7-4d8c-93c3-bea51dac1d77 · outbound

This paper cites Imagenet-trained cnns are biased towards texture; in- creasing shape bias improves accuracy and robustness,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Imagenet-trained cnns are biased towards texture; in- creasing shape bias improves accuracy and robustness,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:03.211391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fb8349c0-5713-4d9d-b5d4-d6c9137209f0 · outbound

This paper cites Spatial frequency analysis in the visual system,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Spatial frequency analysis in the visual system,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:02.986444Z

Source-reported events for the cited work

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Observation bb4613a0-1ba0-428d-b6af-978c1f3901b9 · outbound

This paper cites Distinct spatial frequency sensitivities for processing faces and emotional expressions,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Distinct spatial frequency sensitivities for processing faces and emotional expressions,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:02.799194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2f1bcce0-e886-4524-b53e-dce94988428c · outbound

This paper cites Introducing stable diffusion 3.5,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Introducing stable diffusion 3.5,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:02.589285Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6861587b-27af-4e91-bb7c-596506ac0055 · outbound

This paper cites Stable imagenet-1k dataset,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Stable imagenet-1k dataset,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:02.354294Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e309ae7e-7ceb-4281-b87f-e04b1c33dabc · outbound

This paper cites CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images

Reference 68

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Observation 0917d9f0-2a56-4e18-9b27-bcd0cb0ccb0a · outbound

This paper cites Learning multiple layers of features from tiny images,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Learning multiple layers of features from tiny images,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:02.136374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c6b4b5cc-7335-47a3-977e-f1d3d80ce43d · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image cap- tioning,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image cap- tioning,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:01.934927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3739c272-1042-49df-bc10-9049d4ae9d35 · outbound

This paper cites Microsoft coco: Common objects in context,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Microsoft coco: Common objects in context,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:01.717093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cfdbfdc5-2132-4c87-bcc9-7c87b37b8eff · outbound

This paper cites Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:01.491036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b4d1bede-e084-412d-ac24-69779f4bf2e0 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Imagenet large scale visual recognition challenge,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:49:01.226733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0dfd9bf2-eb4c-478b-a725-fba423b2746c · outbound

This paper cites Qwen2.5-vl,.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations Qwen2.5-vl,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T11:49:00.730675Z

Source-reported events for the cited work

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Observation fe2e8c30-7841-40ba-802c-261fa120293d · outbound

This paper cites LLMs Are Not Yet Ready for Deepfake Image Detection.

On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations LLMs Are Not Yet Ready for Deepfake Image Detection

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T11:48:58.556863Z

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