ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors | Paper Citation Record

Observation 556378e0-a113-4e29-aa34-e2ac47f2e8ef · outbound

This paper cites Deepfakes and beyond: A survey of face manipulation and fake detection,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Deepfakes and beyond: A survey of face manipulation and fake detection,

Reference 1

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Observation d0048519-00ab-4382-b7ca-14c5629e0fcf · outbound

This paper cites The creation and detection of deepfakes: A survey,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors The creation and detection of deepfakes: A survey,

Reference 2

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Observation c2ba8053-a9e8-49f4-ac87-4ffef2a25bba · outbound

This paper cites Deepfake generation and detection: A benchmark and survey,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Deepfake generation and detection: A benchmark and survey,

Reference 3

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Observation c0d3187a-ff68-4503-95dc-377c0952e553 · outbound

This paper cites Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges

Reference 4

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This paper cites Threats and vulnerabilities in artificial intelligence and agentic ai models,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Threats and vulnerabilities in artificial intelligence and agentic ai models,

Reference 5

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Observation 37e07780-1626-4eda-9c67-0eb9ca18eba5 · outbound

This paper cites Deep residual learning for image recognition,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Deep residual learning for image recognition,

Reference 6

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Observation cbf13927-0342-4e66-a3f3-050930776a16 · outbound

This paper cites Densely connected convolutional networks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Densely connected convolutional networks,

Reference 7

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Observation 2bd0a670-d1bd-4c15-87ae-089ce2088c9f · outbound

This paper cites EfficientNet: Rethinking model scaling for convolutional neural networks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors EfficientNet: Rethinking model scaling for convolutional neural networks,

Reference 8

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Observation e87e4c1b-0787-4a5f-a4eb-162b499683a9 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors An image is worth 16x16 words: Trans- formers for image recognition at scale,

Reference 9

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Observation a19ef547-bfbc-4aa8-abd1-330c6b28a4ed · outbound

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

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Swin Transformer: Hierarchical vision transformer using shifted win- dows,

Reference 10

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Observation 60b56041-0318-40e9-a3b7-89c0524ddf98 · outbound

This paper cites Can pretrained face verification models distinguish true identity from deepfakes?.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Can pretrained face verification models distinguish true identity from deepfakes?

Reference 11

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Observation fa3ec7b8-daeb-4e2b-b4f7-b3f2b83770df · outbound

This paper cites FaceForensics++: Learning to detect manipulated facial images,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors FaceForensics++: Learning to detect manipulated facial images,

Reference 12

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Observation cbe5741e-b620-4b00-a5ca-3d3428405384 · outbound

This paper cites Intriguing properties of neural networks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Intriguing properties of neural networks,

Reference 13

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Observation 988627d3-6732-4ab9-aa9d-62cf5ede2637 · outbound

This paper cites Explaining and harnessing adversarial examples,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Explaining and harnessing adversarial examples,

Reference 14

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Observation 0ffdd397-e71f-4d3f-b707-c6a048d9c308 · outbound

This paper cites Threat of adversarial attacks on deep learning in computer vision: A survey,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Threat of adversarial attacks on deep learning in computer vision: A survey,

Reference 15

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Observation 83e396b8-989b-49f5-840c-70883fbd3a3e · outbound

This paper cites Revisiting transferable adversarial images: Systemization, evaluation, and new insights,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Revisiting transferable adversarial images: Systemization, evaluation, and new insights,

Reference 16

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Observation b988f4c8-1972-4702-b777-1bc8c3ebdf5b · outbound

This paper cites Boosting ad- versarial attacks with momentum,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Boosting ad- versarial attacks with momentum,

Reference 17

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Observation 9e935b92-eea5-458c-8f1a-2a64c2c93db2 · outbound

This paper cites Improving transferability of adversarial examples with input diversity,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Improving transferability of adversarial examples with input diversity,

Reference 18

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Observation 3e0df747-ceac-44b5-8cec-3b434cfd1910 · outbound

This paper cites Evading defenses to trans- ferable adversarial examples by translation-invariant attacks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Evading defenses to trans- ferable adversarial examples by translation-invariant attacks,

Reference 19

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Observation 83b67a7f-bf67-4b93-b22b-4db660852c64 · outbound

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

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Practical black-box attacks against machine learning,

Reference 20

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Observation 554b6b14-aea5-448f-a541-ae7b9c40cab3 · outbound

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

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Square At- tack: A query-efficient black-box adversarial attack via random search,

Reference 21

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Observation 81a03461-d535-4819-9c65-92287becb9a8 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 22

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Observation 23539877-58cd-4bbe-b13d-bdf560e9bfd4 · outbound

This paper cites Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning

Reference 23

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Observation 8c12bbfe-adac-47a9-9070-2ef8e26347b8 · outbound

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

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors CNN- generated images are surprisingly easy to spot... for now,

Reference 24

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Observation 096e2cf9-a174-46a0-924b-104d0481b126 · outbound

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

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Thinking in frequency: Face forgery detection by mining frequency-aware clues,

Reference 25

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Observation 837568c8-76d0-4deb-a701-c90cbb63fc0f · outbound

This paper cites Intriguing properties of vision transformers,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Intriguing properties of vision transformers,

Reference 26

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Observation 996d64e9-105b-41dd-93ba-7fdbcdb59926 · outbound

This paper cites Towards transferable adversarial attacks on vision transformers,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Towards transferable adversarial attacks on vision transformers,

Reference 27

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Observation 65f6467d-087f-41a8-b75b-0bcfa9cc3432 · outbound

This paper cites Frequency domain model augmentation for adversarial attack,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Frequency domain model augmentation for adversarial attack,

Reference 28

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Observation 599153a9-028d-4bde-9d1e-7dadd9a9584a · outbound

This paper cites Boosting adversarial transferability by block shuffle and rotation,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Boosting adversarial transferability by block shuffle and rotation,

Reference 29

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Observation 2ab415d2-6436-438a-ac95-3ebcb7d3b779 · outbound

This paper cites ARMOR: Agentic reasoning for methods orchestration and reparameterization for robust adversarial attacks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors ARMOR: Agentic reasoning for methods orchestration and reparameterization for robust adversarial attacks,

Reference 30

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Observation 58dc9a06-905d-4f34-9379-d55c1ec7509e · outbound

This paper cites Agentic AI: Autonomous intelligence for complex goals — a comprehensive survey,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Agentic AI: Autonomous intelligence for complex goals — a comprehensive survey,

Reference 31

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Observation 6ebf2fce-2271-490a-9f5c-ae9d4642d488 · outbound

This paper cites Multi-Agent Collaboration Mechanisms: A Survey of LLMs.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Multi-Agent Collaboration Mechanisms: A Survey of LLMs

Reference 32

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This paper cites Agentic AI: A com- prehensive survey of architectures, applications, and future directions,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Agentic AI: A com- prehensive survey of architectures, applications, and future directions,

Reference 33

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Observation 4b945bdd-d76e-4b12-aa54-d30a11283aee · outbound

This paper cites Qwen2.5-VL Technical Report.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Qwen2.5-VL Technical Report

Reference 34

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This paper cites Qwen3 Technical Report.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Qwen3 Technical Report

Reference 35

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

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Towards evaluating the robustness of neural networks,

Reference 36

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Observation b0d81842-cd6b-4c6e-8078-4f4082b9294f · outbound

This paper cites The limitations of deep learning in adversarial settings,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors The limitations of deep learning in adversarial settings,

Reference 37

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Observation 8b7e3c6f-6f0b-475f-8540-70761a7f727c · outbound

This paper cites Spatially transformed adversarial examples,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Spatially transformed adversarial examples,

Reference 38

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Observation 16f7bc03-1cf5-483a-9206-709cb339d9d0 · outbound

This paper cites Adversarial attacks on deepfake detec- tors: A challenge in the era of AI-generated media (AADD-2025),.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Adversarial attacks on deepfake detec- tors: A challenge in the era of AI-generated media (AADD-2025),

Reference 39

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This paper cites The Deepfake Detection Challenge (DFDC) Preview Dataset.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors The Deepfake Detection Challenge (DFDC) Preview Dataset

Reference 40

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ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Multi- attentional deepfake detection,

Reference 41

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Observation 8223f427-546c-4c0f-a5b9-59bfe602da8f · outbound

This paper cites Detecting deepfakes with self-blended images,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Detecting deepfakes with self-blended images,

Reference 42

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Observation 7f9409d9-038c-4114-a80d-126ba34d9896 · outbound

This paper cites DeepfakeBench: A comprehensive benchmark of deepfake detection,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors DeepfakeBench: A comprehensive benchmark of deepfake detection,

Reference 43

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Observation b7353f6b-7289-430c-85d7-6e930c25df5f · outbound

This paper cites Zero-shot detection of AI-generated images,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Zero-shot detection of AI-generated images,

Reference 44

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Observation 0a394d77-988a-464c-8e1d-40c5eaa3cd80 · outbound

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ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Evading deepfake-image detectors with white- and black-box attacks,

Reference 45

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Observation 1b4f4d38-7d8f-4879-8d15-096ceb002771 · outbound

This paper cites Adversarial perturbations fool deepfake detec- tors,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Adversarial perturbations fool deepfake detec- tors,

Reference 46

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Observation 42f2dff8-7cf7-4df0-b0de-16977f81607a · outbound

This paper cites Adversarial attack on deepfake detection using RL-based texture patches,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Adversarial attack on deepfake detection using RL-based texture patches,

Reference 47

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Observation 34d5b80a-3b07-4569-b74b-9acbcf556246 · outbound

This paper cites 2D-Malafide: Adversarial attacks against face deepfake detection systems,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors 2D-Malafide: Adversarial attacks against face deepfake detection systems,

Reference 48

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Observation eea5ca9c-5107-4316-88ef-a48a0ca9a68e · outbound

This paper cites MIG-COW: Transferable adversarial attacks on deepfake detectors via gradient decomposition,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors MIG-COW: Transferable adversarial attacks on deepfake detectors via gradient decomposition,

Reference 49

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Observation 51425b62-c7a3-4255-aac4-780587b566ef · outbound

This paper cites MS-GAGA: Metric-selective guided adversarial generation attack,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors MS-GAGA: Metric-selective guided adversarial generation attack,

Reference 50

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Observation 34236ede-a0b0-4b19-ba9b-39df2814f93b · outbound

This paper cites Delving into transferable adversarial examples and black-box attacks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Delving into transferable adversarial examples and black-box attacks,

Reference 51

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Observation 33f9f9eb-3f1c-41db-b690-bd5deeb208ed · outbound

This paper cites Nesterov accelerated gradient and scale invariance for adversarial attacks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Nesterov accelerated gradient and scale invariance for adversarial attacks,

Reference 52

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Observation 5bd683ef-faee-42b6-8352-2074c25fc12d · outbound

This paper cites Simple black-box adversarial attacks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Simple black-box adversarial attacks,

Reference 53

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Observation 5ce10af3-644a-4f0f-92d1-24d04aac5f8b · outbound

This paper cites Efficient black-box adversarial attacks via bayesian optimization guided by a function prior,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Efficient black-box adversarial attacks via bayesian optimization guided by a function prior,

Reference 54

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Observation 8620ef02-f307-49d1-a808-7c3f9abb3008 · outbound

This paper cites Feature importance-aware transferable adversarial attacks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Feature importance-aware transferable adversarial attacks,

Reference 55

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Observation 511a90be-f108-4f14-a522-da4b65be0eff · outbound

This paper cites AnyAttack: Towards large-scale self-supervised adversarial attacks on vision-language models,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors AnyAttack: Towards large-scale self-supervised adversarial attacks on vision-language models,

Reference 56

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Observation 9c3f8dd3-b9ae-4278-b278-9bd9f2590982 · outbound

This paper cites Semantic-aligned adversarial evolution triangle for high- transferability vision-language attack,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Semantic-aligned adversarial evolution triangle for high- transferability vision-language attack,

Reference 57

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Observation 27a8c3cc-4f11-4db4-8d02-6b907b76d107 · outbound

This paper cites Adversarial attacks against closed-source MLLMs via feature optimal alignment,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Adversarial attacks against closed-source MLLMs via feature optimal alignment,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:16.905745Z

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Observation 800a6abd-fbcf-4309-8716-a34040082900 · outbound

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ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors AutoGen: Enabling next-gen LLM applications via multi- agent conversation,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.018301Z

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Observation 1e864c2e-3c06-4c7e-9a07-999f4e9c9c3f · outbound

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ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Generative agents: Interactive simulacra of human behavior,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.124241Z

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Observation 8a773500-1ff7-4b03-ab89-bd2a259e2db2 · outbound

This paper cites The rise and potential of large language model based agents: A survey,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors The rise and potential of large language model based agents: A survey,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.203457Z

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Observation 123b2c44-767c-44cf-a40b-6b2e38926fdf · outbound

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ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Large language models as optimizers,

Reference 62

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Observation f146a8dd-7a91-4f34-8d72-9bc9f31c6b32 · outbound

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ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors AgentHPO: Large language model agent for hyper-parameter optimization,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.399511Z

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Observation a57b8858-4fa3-4481-a11e-05b8572a5b72 · outbound

This paper cites AutoDA: Automated decision-based iterative adversarial attacks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors AutoDA: Automated decision-based iterative adversarial attacks,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.473100Z

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Observation 4143cc4b-a9fa-4186-ab33-260e892f0d17 · outbound

This paper cites L-AutoDA: Large language models for automatically evolving decision-based adversarial attacks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors L-AutoDA: Large language models for automatically evolving decision-based adversarial attacks,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.553122Z

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Observation 064ae00d-5c67-4034-b468-821d97cefaf1 · outbound

This paper cites Red-teaming LLM multi-agent systems via communication attacks,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Red-teaming LLM multi-agent systems via communication attacks,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.627809Z

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Observation b2a46116-8d91-4c52-bbac-ffb11feaef6b · outbound

This paper cites Image quality assessment: From error visibility to structural similarity,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Image quality assessment: From error visibility to structural similarity,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.705381Z

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Observation 367754c0-6147-4034-9330-90e07884e9d8 · outbound

This paper cites Probable inference, the law of succession, and statistical inference,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Probable inference, the law of succession, and statistical inference,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.801984Z

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Observation 2399c185-5a84-407d-a0db-91b96e2a064b · outbound

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

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Towards deep learning models resistant to adversarial attacks,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.870113Z

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Observation 6c169e95-910e-48c9-b8b2-b5ab6228c057 · outbound

This paper cites Mitigating adversarial effects through randomization,.

ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors Mitigating adversarial effects through randomization,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-01T23:49:17.980388Z

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