TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors | Paper Citation Record

Observation 3313d70c-4eef-439c-b98a-48e64c51b632 · outbound

This paper cites Denoising diffusion probabilistic models,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Denoising diffusion probabilistic models,

Reference 1

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Observation b05f96a8-bae2-46c5-9076-49cc25fdf799 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors High- resolution image synthesis with latent diffusion models,

Reference 2

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Observation 62250858-20d3-48c7-b20a-45b095504791 · outbound

This paper cites Collaborative diffusion for multi-modal face generation and editing,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Collaborative diffusion for multi-modal face generation and editing,

Reference 3

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Observation 309b3e8b-b371-4412-ab77-4a934befe55c · outbound

This paper cites Defensive adversarial captcha: A semantics- driven framework for natural adversarial example generation,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Defensive adversarial captcha: A semantics- driven framework for natural adversarial example generation,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:53.217115Z

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Observation 9a8e2f0f-9f27-4e3c-ab81-80ad36c641f3 · outbound

This paper cites State-aware perturbation optimization for robust deep reinforcement learning,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors State-aware perturbation optimization for robust deep reinforcement learning,

Reference 5

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Observation 2c05ceaf-c0c0-4ed6-b58d-b6dd970832c6 · outbound

This paper cites LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systems,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systems,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:53.503852Z

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Observation c0041a79-dc1f-4535-a9c5-1de2437608ea · outbound

This paper cites A deep learning approach to universal image manipulation detection using a new convolutional layer,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors A deep learning approach to universal image manipulation detection using a new convolutional layer,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:53.651950Z

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Observation 5fa34824-cd88-4e5f-9ac4-fb40e1661a1f · outbound

This paper cites Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Lan- guage Models,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Lan- guage Models,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:53.810930Z

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Observation aabe1403-1485-4a48-964e-59a0e96db907 · outbound

This paper cites CNN- generated images are surprisingly easy to spot... for now,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors CNN- generated images are surprisingly easy to spot... for now,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:53.958253Z

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Observation 8410db36-1775-45e8-98d8-57d27697aa75 · outbound

This paper cites SatSense: Multi-Satellite Collaborative Framework for Spectrum Sensing,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors SatSense: Multi-Satellite Collaborative Framework for Spectrum Sensing,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:54.128290Z

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Observation 0d47eb99-20ea-479c-bd1f-c70287b9e8ff · outbound

This paper cites SUMS: Sniffing Unknown Multiband Signals under Low Sampling Rates,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors SUMS: Sniffing Unknown Multiband Signals under Low Sampling Rates,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:54.284321Z

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Observation 97f70a76-038f-41e6-8dfa-7e1b206fac39 · outbound

This paper cites Efficient Parallel Split Learning over Resource-Constrained Wireless Edge Networks,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Efficient Parallel Split Learning over Resource-Constrained Wireless Edge Networks,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:54.406413Z

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Observation 898a967a-fb71-4f61-b8a4-07a9957c6dc9 · outbound

This paper cites Towards universal fake image detec- tors that generalize across generative models,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Towards universal fake image detec- tors that generalize across generative models,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:54.570527Z

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Observation 445952a6-a66e-4de9-8b9d-1bcb815ef093 · outbound

This paper cites Unionformer: Unified-learning transformer with multi-view representation for im- age manipulation detection and localization,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Unionformer: Unified-learning transformer with multi-view representation for im- age manipulation detection and localization,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:54.783662Z

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Observation e5361bb0-0306-4fcd-bae4-4c1c49eb955a · outbound

This paper cites Gapsl: A gradient-aligned parallel split learning on heterogeneous data,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Gapsl: A gradient-aligned parallel split learning on heterogeneous data,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:54.908748Z

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Observation c2f55e32-437e-4b8b-8aad-e09b2ddb0049 · outbound

This paper cites Hfedmoe: Resource-aware heterogeneous federated learning with mixture-of-experts,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Hfedmoe: Resource-aware heterogeneous federated learning with mixture-of-experts,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:55.029329Z

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Observation e73113ac-7fba-4233-970c-ceee31cb0a02 · outbound

This paper cites Thinking in frequency: Face forgery detection by mining frequency-aware clues,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Thinking in frequency: Face forgery detection by mining frequency-aware clues,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:55.154845Z

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Observation 71bc1562-92f1-4670-a012-ddaa1f83bf6f · outbound

This paper cites Rethinking the up- sampling operations in CNN-based generative network for generalizable 13 deepfake detection,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Rethinking the up- sampling operations in CNN-based generative network for generalizable 13 deepfake detection,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:55.268181Z

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Observation 8b184278-f750-4358-a91b-6b7d1c3f3994 · outbound

This paper cites Dire for diffusion-generated image detection,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Dire for diffusion-generated image detection,

Reference 19

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Observation 1b736b46-b64b-4b64-8a03-48977e00dde6 · outbound

This paper cites LaRE 2: Latent reconstruction error based method for diffusion-generated image detection,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors LaRE 2: Latent reconstruction error based method for diffusion-generated image detection,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:55.523838Z

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Observation ff0614d9-f15c-4835-9de3-f1006392ce91 · outbound

This paper cites Evading Deepfake-Image Detectors with White- and Black-Box Attacks.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Evading Deepfake-Image Detectors with White- and Black-Box Attacks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:55.635387Z

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Observation 9e3f7d44-2a56-4d03-9ac6-a972cd2cc514 · outbound

This paper cites Vulnerabilities in AI-generated image detection: The challenge of adversarial attacks,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Vulnerabilities in AI-generated image detection: The challenge of adversarial attacks,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:55.748895Z

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Observation 28b7b9b7-9070-48f1-a519-ad5cfd4733aa · outbound

This paper cites Take fake as real: Realistic-like robust black-box adversarial attack to evade AIGC detection,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Take fake as real: Realistic-like robust black-box adversarial attack to evade AIGC detection,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:55.875282Z

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Observation cdb313c7-e367-4ea2-b5a7-9437f2e4c6e9 · outbound

This paper cites StealthDif- fusion: Towards evading diffusion forensic detection through diffusion model,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors StealthDif- fusion: Towards evading diffusion forensic detection through diffusion model,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:56.018537Z

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Observation 7ed8b706-8361-4088-91a6-29c32bf7919d · outbound

This paper cites Adversarial diffusion model: Generating high quality and undetectable images from scratch,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Adversarial diffusion model: Generating high quality and undetectable images from scratch,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:56.188146Z

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Observation d8932938-410f-4f37-ac45-81fba6f5ef6b · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:56.344873Z

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Observation 9eb541a9-128c-4983-a9df-cc3277c7880c · outbound

This paper cites A style-based generator architecture for generative adversarial networks,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors A style-based generator architecture for generative adversarial networks,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:56.446594Z

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Observation f7967cf4-9973-498a-a78d-da105dbca8ad · outbound

This paper cites Channel Power Gain Estimation for Terahertz Vehicle-to-Infrastructure Networks,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Channel Power Gain Estimation for Terahertz Vehicle-to-Infrastructure Networks,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:56.594329Z

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Observation 8edc57f5-38de-44b8-93b0-e52e67ad987b · outbound

This paper cites Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:56.681415Z

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Observation 8b4f5904-f5b6-48ea-9da2-9adc0b234747 · outbound

This paper cites CAMD: Context- aware masked distillation for general self-supervised facial representa- tion pre-training,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors CAMD: Context- aware masked distillation for general self-supervised facial representa- tion pre-training,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:56.738073Z

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Observation 940e3e46-b4cf-497f-a2ef-811d832b9190 · outbound

This paper cites Mobile edge intelligence for large language models: A contemporary survey,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Mobile edge intelligence for large language models: A contemporary survey,

Reference 31

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Observation b0e1a346-6627-4648-9c0e-715f3c8105e9 · outbound

This paper cites HASFL: Heterogeneity- aware Split Federated Learning over Edge Computing Systems,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors HASFL: Heterogeneity- aware Split Federated Learning over Edge Computing Systems,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:56.870054Z

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Observation 1e451f6a-5964-4f6a-a0b0-2c79a5bfe805 · outbound

This paper cites Global texture enhancement for fake face detection in the wild,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Global texture enhancement for fake face detection in the wild,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:56.946488Z

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Observation cb12c98a-603f-4b02-b53e-e6ace29b161c · outbound

This paper cites Image manipulation detection by multi-view multi-scale supervision,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Image manipulation detection by multi-view multi-scale supervision,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:57.106345Z

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Observation f6d08d17-6dd8-4bab-8bcb-bab78de55543 · outbound

This paper cites Dual frequency branch frame- work with reconstructed sliding windows attention for AI-generated image detection,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Dual frequency branch frame- work with reconstructed sliding windows attention for AI-generated image detection,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:57.266553Z

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Observation cfd61d44-24ea-44e5-ac72-37745da4578c · outbound

This paper cites Frequency-aware deepfake detection: Improving generalizability through frequency space learning,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Frequency-aware deepfake detection: Improving generalizability through frequency space learning,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:57.514165Z

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Observation 7627f607-8085-4316-bd18-1da198b39978 · outbound

This paper cites A sanity check for AI-generated image detection,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors A sanity check for AI-generated image detection,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:57.625806Z

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Observation fdee8d83-6365-4a19-a0b7-6ab9522a2b2e · outbound

This paper cites Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:57.733225Z

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Observation 1a2ba999-3788-420e-b46a-d92e911d9513 · outbound

This paper cites IMDL-BenCo: A comprehensive benchmark and codebase for image manipulation detection & localization,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors IMDL-BenCo: A comprehensive benchmark and codebase for image manipulation detection & localization,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:57.784977Z

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Observation 4f0d7043-a722-45fb-b6b7-0a91432fb798 · outbound

This paper cites ForensicHub: A unified benchmark & codebase for all-domain fake image detection and localization,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors ForensicHub: A unified benchmark & codebase for all-domain fake image detection and localization,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:57.861820Z

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Observation 1f29a543-913e-40b0-9934-fe222c1470ad · outbound

This paper cites Is artificial intelligence generated image detection a solved problem?.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Is artificial intelligence generated image detection a solved problem?

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:57.965474Z

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Observation 7341d947-bff0-4e9e-9f18-db82e2b9f4ea · outbound

This paper cites Genimage: A million-scale benchmark for detecting AI- generated image,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Genimage: A million-scale benchmark for detecting AI- generated image,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.023163Z

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Observation 3b845410-ad3f-4dfc-81a3-2eff5ab618f1 · outbound

This paper cites Revisiting image manipulation localization under realistic manipulation scenarios,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Revisiting image manipulation localization under realistic manipulation scenarios,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.064921Z

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Observation dfc2e0b4-ea3b-47c6-bec5-f6876e7fa2c4 · outbound

This paper cites AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors AEROBLADE: Training-free detection of latent diffusion images using autoencoder reconstruction error,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.137456Z

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Observation af0c66a5-d6cd-4a04-98a9-3e4aeb32891e · outbound

This paper cites DRCT: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors DRCT: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.220471Z

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Observation e663a541-a34a-4d66-be57-03c8575ea3c5 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Explaining and Harnessing Adversarial Examples

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.292084Z

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Observation e298c498-9e13-49a4-aeb0-ff275c2dc357 · outbound

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

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Towards deep learning models resistant to adversarial attacks,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.373605Z

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Observation 16affd31-bbb6-4470-8e2f-2c8e5aec9b24 · outbound

This paper cites Towards evaluating the robustness of neural networks,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Towards evaluating the robustness of neural networks,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.448871Z

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Observation 37638f85-a2f7-4a80-b1f0-aa778b7da28f · outbound

This paper cites Practical black-box attacks against machine learning,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Practical black-box attacks against machine learning,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.530470Z

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Observation 6fcb1b64-9fc4-433b-9987-886374c66556 · outbound

This paper cites Boosting adversarial attacks with momentum,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Boosting adversarial attacks with momentum,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.586193Z

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Observation 1d270d0e-c7dc-49e1-8316-bb82df3b08a8 · outbound

This paper cites Simple black-box adversarial attacks,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Simple black-box adversarial attacks,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.653436Z

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Observation 2ab1a43e-a8c0-471e-ab76-c69cb2afdbb6 · outbound

This paper cites Square at- tack: A query-efficient black-box adversarial attack via random search,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Square at- tack: A query-efficient black-box adversarial attack via random search,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.755797Z

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Observation 27068082-94fb-413f-a8b7-97a676b6d274 · outbound

This paper cites BRUSLEATTACK: A query-efficient score-based black-box sparse adversarial attack,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors BRUSLEATTACK: A query-efficient score-based black-box sparse adversarial attack,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.837882Z

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Observation d3343a69-ae63-46b9-a966-9ac15918b0c6 · outbound

This paper cites DP-RAE: A dual-phase merging reversible adversarial example for image privacy protection,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors DP-RAE: A dual-phase merging reversible adversarial example for image privacy protection,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.917194Z

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Observation e28c1453-77a6-4fd4-9c24-6d94c1178060 · outbound

This paper cites Guided evolutionary strategies: Augmenting random search with surrogate gradients,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Guided evolutionary strategies: Augmenting random search with surrogate gradients,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:58.995988Z

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Observation 846632fb-a8f1-49fb-ad53-b8d7ca2c5f05 · outbound

This paper cites Improving black- box adversarial attacks with a transfer-based prior,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Improving black- box adversarial attacks with a transfer-based prior,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:59.077293Z

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Observation e5389de8-ef75-4f27-a2bb-b085aa08b3f7 · outbound

This paper cites Adversarial Deepfakes: Evaluating vulnerability of Deepfake detectors to adversarial examples,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Adversarial Deepfakes: Evaluating vulnerability of Deepfake detectors to adversarial examples,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:59.145301Z

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Observation 4e5185ba-5501-44b6-a314-4ec679beaa80 · outbound

This paper cites Evading Deep- fake detectors via adversarial statistical consistency,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Evading Deep- fake detectors via adversarial statistical consistency,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:59.194927Z

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Observation 632520a5-a857-4e74-8bc6-5a63896131b2 · outbound

This paper cites MaskGAN: Towards diverse and interactive facial image manipulation,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors MaskGAN: Towards diverse and interactive facial image manipulation,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:59.236911Z

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Observation 96894722-f037-4cda-976c-dfd645f5bf8e · outbound

This paper cites Deep residual learning for image recognition,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Deep residual learning for image recognition,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:59.280156Z

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Observation 419af507-011a-48fc-871d-7b06be3aa731 · outbound

This paper cites EfficientNet: Rethinking model scaling for con- volutional neural networks,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors EfficientNet: Rethinking model scaling for con- volutional neural networks,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:59.283406Z

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Observation 3d872d4a-38ca-4d53-927b-fad8b5f2e69e · outbound

This paper cites Training data-efficient image transformers & distillation through attention,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Training data-efficient image transformers & distillation through attention,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:59.293188Z

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Observation 1c2221f1-38d3-48f1-998e-99de748e65d0 · outbound

This paper cites Swin Transformer: Hierarchical vision transformer using shifted win- dows,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Swin Transformer: Hierarchical vision transformer using shifted win- dows,

Reference 63

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Observation d5796985-65e1-40c0-9274-6936951563e5 · outbound

This paper cites Orthogonal subspace decomposition for generalizable AI-generated image detection,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors Orthogonal subspace decomposition for generalizable AI-generated image detection,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:59.592071Z

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Observation 490c3afb-b17f-414c-b066-2983915a4996 · outbound

This paper cites PGC: Peak-guided calibration for generalizable AI-generated image detection,.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors PGC: Peak-guided calibration for generalizable AI-generated image detection,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-01T01:14:59.819915Z

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Observation e38d0f9a-bc2c-4ec9-a934-41e3b9bc37aa · outbound

This paper cites 70 268–70 288.

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors 70 268–70 288

Reference 267

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