ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks | Paper Citation Record

Observation 3d771a02-c497-44be-91e0-2ea3f2c8d295 · outbound

This paper cites LeapFrog: The Rowhammer Instruction Skip Attack.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks LeapFrog: The Rowhammer Instruction Skip Attack

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:26:08.232872Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 999d720e-53c4-4909-bb20-82191fe5a5d9 · outbound

This paper cites Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T04:26:07.095253Z

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Observation fe102567-206d-46bb-baaa-6d97dd208398 · outbound

This paper cites Practical fault attack on deep neural networks.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Practical fault attack on deep neural networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:10.098247Z

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Observation 86d2e996-5b8d-4c66-89b1-97319e106571 · outbound

This paper cites Deepattest: An end-to-end attestation framework for deep neural networks.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Deepattest: An end-to-end attestation framework for deep neural networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:10.081269Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cd529856-1be1-422a-a0b5-686dc1a2f418 · outbound

This paper cites Proflip: Targeted trojan attack with progressive bit flips.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Proflip: Targeted trojan attack with progressive bit flips

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:26:07.313740Z

Source-reported events for the cited work

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Observation c09fbf17-db69-47b2-b048-871a5fe8b13b · outbound

This paper cites In13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pages 578–594, 2018.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks In13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pages 578–594, 2018

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:10.055077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4f0479ba-89f3-4fbd-aafd-c02908e00a13 · outbound

This paper cites Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces in DNN Executables.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces in DNN Executables

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:26:08.178334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b5a3d86d-a541-4e1c-a0bd-0ef869dedff3 · outbound

This paper cites Bitshield: Defending against bit-flip attacks on dnn executables.computing, 2:47.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Bitshield: Defending against bit-flip attacks on dnn executables.computing, 2:47

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:10.037418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f2e4aee9-2b50-4c65-8a4e-607ff82eaca8 · outbound

This paper cites Real time detection of cache- based side-channel attacks using hardware performance counters.Applied Soft Computing, 49:1162–1174, 2016.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Real time detection of cache- based side-channel attacks using hardware performance counters.Applied Soft Computing, 49:1162–1174, 2016

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:10.019802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 90ede090-f10f-4699-a6fe-2c553ecdbdeb · outbound

This paper cites Exploiting correcting codes: On the effectiveness of ecc memory against rowhammer attacks.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Exploiting correcting codes: On the effectiveness of ecc memory against rowhammer attacks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:10.000404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ba22492c-8202-4bb9-8fad-f944a031ace1 · outbound

This paper cites Trrespass: Exploiting the many sides of target row refresh.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Trrespass: Exploiting the many sides of target row refresh

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.983192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fe234b42-2dfc-4f38-bba8-367d43fca7d9 · outbound

This paper cites Hammerdodger: a light- weight defense framework against rowhammer attack on dnns.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Hammerdodger: a light- weight defense framework against rowhammer attack on dnns

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.965680Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 975319cb-10cd-4110-8687-4a3670865278 · outbound

This paper cites Flush+ flush: a fast and stealthy cache attack.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Flush+ flush: a fast and stealthy cache attack

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.949488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2b84eafa-f34f-4ebb-9f7b-67066f0c08b2 · outbound

This paper cites Deep residual learning for image recognition.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Deep residual learning for image recognition

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T04:26:07.847542Z

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Unavailable: canonical work link unavailable.

Observation 3351bc3d-846b-463a-ba3b-3755c4455f8e · outbound

This paper cites Defending and harnessing the bit-flip based adversarial weight attack.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Defending and harnessing the bit-flip based adversarial weight attack

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.922450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2a7c2f7c-d4f2-43af-96d5-b2cb3dd8dc10 · outbound

This paper cites Profile-guided automated software diversity.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Profile-guided automated software diversity

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.907106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 562f2c9b-d6d3-45f2-8c13-7df6f3478a8b · outbound

This paper cites Safe- guarding the intelligence of neural networks with built-in light-weight integrity marks (lima).

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Safe- guarding the intelligence of neural networks with built-in light-weight integrity marks (lima)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.889156Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 740776e5-3008-4075-8672-4dad0cb643c6 · outbound

This paper cites Detection of traffic signs in real-world images: The german traffic sign detection benchmark.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Detection of traffic signs in real-world images: The german traffic sign detection benchmark

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.873562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 85d92774-b813-49e3-9bfe-f3f85e1c9c63 · outbound

This paper cites Reverse engineering convolutional neural networks through side-channel information leaks.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Reverse engineering convolutional neural networks through side-channel information leaks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.856209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e1ea446b-95f5-4297-93b6-7c81874bfdd7 · outbound

This paper cites Mascat: Stopping microar- chitectural attacks before execution.Cryptology ePrint Archive, 2016.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Mascat: Stopping microar- chitectural attacks before execution.Cryptology ePrint Archive, 2016

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.839801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 27ec22b5-c248-43a0-8d66-e6a1de4c7573 · outbound

This paper cites Acchashtag: Accel- erated hashing for detecting fault-injection attacks on embedded neural networks.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Acchashtag: Accel- erated hashing for detecting fault-injection attacks on embedded neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.817586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 267e1358-2a7e-4554-a53a-4bf5a7a8fcc8 · outbound

This paper cites Hashtag: Hash signatures for online detection of fault-injection attacks on deep neural networks.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Hashtag: Hash signatures for online detection of fault-injection attacks on deep neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.794412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bab4278e-da97-4457-8e69-9b679240b6b9 · outbound

This paper cites Machine learning-based rowhammer mitigation.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 42(5):1393–1405, 2022.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Machine learning-based rowhammer mitigation.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 42(5):1393–1405, 2022

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.777123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6ccc51be-9b7a-4122-abe4-026a49bd2ced · outbound

This paper cites Flipping bits in memory with- out accessing them: An experimental study of dram disturbance errors.ACM SIGARCH Computer Architecture News, 42(3):361–372, 2014.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Flipping bits in memory with- out accessing them: An experimental study of dram disturbance errors.ACM SIGARCH Computer Architecture News, 42(3):361–372, 2014

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.761596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6b02c08c-d8ab-4673-991e-4de098601870 · outbound

This paper cites In13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pages 697–710, 2018.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks In13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pages 697–710, 2018

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.745373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 03c736a4-0f49-4220-b8dd-a1f048091c71 · outbound

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

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Learning multiple layers of features from tiny images

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T04:26:07.930764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7ab5ce3-9d5b-4f17-a7c6-9d6e92b34d5f · outbound

This paper cites Sok: Auto- mated software diversity.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Sok: Auto- mated software diversity

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.720081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8154f857-2092-4506-85a3-5d4d88fafc43 · outbound

This paper cites Neurobfuscator: A full-stack obfuscation tool to mitigate neural architecture stealing.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Neurobfuscator: A full-stack obfuscation tool to mitigate neural architecture stealing

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.706097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ea06645e-ec02-4117-afb3-567e29afe337 · outbound

This paper cites Radar: Run-time adversarial weight attack detection and accuracy recovery.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Radar: Run-time adversarial weight attack detection and accuracy recovery

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.691521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9280aaa1-2110-4196-ab76-4c4467ee02e2 · outbound

This paper cites Defending bit-flip attack through dnn weight reconstruction.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Defending bit-flip attack through dnn weight reconstruction

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.677024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 617bd89c-548e-4e7b-bdd9-6430629e9576 · outbound

This paper cites Yes, one-bit-flip matters! universal dnn model inference depletion with runtime code fault injection.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Yes, one-bit-flip matters! universal dnn model inference depletion with runtime code fault injection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.660515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 87d5f2c0-d7aa-4dd8-931e-36b1019a742f · outbound

This paper cites Deepdyve: Dynamic verification for deep neural networks.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Deepdyve: Dynamic verification for deep neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.646018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6bb4112d-8415-4ca9-85df-0aca5ffa6cae · outbound

This paper cites Generating robust dnn with resistance to bit-flip based adversarial weight attack.IEEE Transactions on Computers, 72(2):401–413, 2022.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Generating robust dnn with resistance to bit-flip based adversarial weight attack.IEEE Transactions on Computers, 72(2):401–413, 2022

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.629159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cf44f97d-1526-46ad-88b3-3332e88818df · outbound

This paper cites Concurrent weight encoding-based detec- tion for bit-flip attack on neural network accelerators.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Concurrent weight encoding-based detec- tion for bit-flip attack on neural network accelerators

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.614741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c483991d-c332-46ae-a7eb-8de839faeb7a · outbound

This paper cites {NeuroPots}: Realtime proactive defense against{Bit-Flip} attacks in neural networks.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks {NeuroPots}: Realtime proactive defense against{Bit-Flip} attacks in neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.599851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e8d3806f-dc50-447b-bf16-72446b373a1a · outbound

This paper cites Fault injection attack on deep neural network.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Fault injection attack on deep neural network

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.583527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c80edf11-b17f-4dea-8c77-715b060fb61b · outbound

This paper cites Siloz: Leveraging dram isolation domains to prevent inter-vm rowhammer.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Siloz: Leveraging dram isolation domains to prevent inter-vm rowhammer

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.569115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1e568032-538f-4bef-aa3b-9cc26738faba · outbound

This paper cites Deepshuffle: A lightweight defense framework against adversarial fault injection attacks on deep neural networks in multi-tenant cloud-fpga.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Deepshuffle: A lightweight defense framework against adversarial fault injection attacks on deep neural networks in multi-tenant cloud-fpga

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.554293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ff223562-c918-42be-86f5-1d1620bdb005 · outbound

This paper cites Rowhammer: A retrospective.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(8):1555–1571, 2019.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Rowhammer: A retrospective.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(8):1555–1571, 2019

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.538950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a1bb027a-f30f-4cc1-be8e-5025e41e4f45 · outbound

This paper cites Bit-flip attack: Crushing neural network with progressive bit search.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Bit-flip attack: Crushing neural network with progressive bit search

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.523871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c1225cfe-d08d-4b23-9275-315a7e42f684 · outbound

This paper cites Tbt: Targeted neural network attack with bit trojan.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Tbt: Targeted neural network attack with bit trojan

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.509834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation df4fc1d4-8dd7-427b-a0ad-c034d2e57659 · outbound

This paper cites T-bfa: Targeted bit-flip adversarial weight attack.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):7928–7939, 2021.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks T-bfa: Targeted bit-flip adversarial weight attack.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):7928–7939, 2021

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.488542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ca74eaa6-8bfc-4d32-a59f-425ca6572eeb · outbound

This paper cites In30th USENIX Security Symposium (USENIX Security 21), pages 1919–1936, 2021.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks In30th USENIX Security Symposium (USENIX Security 21), pages 1919–1936, 2021

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.286958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 449698d9-a170-4934-87d5-ef8d1482fd1c · outbound

This paper cites RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T04:26:08.020924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e21cc962-c11e-4608-b4e0-407895598336 · outbound

This paper cites Flip feng shui: Hammering a needle in the software stack.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Flip feng shui: Hammering a needle in the software stack

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:09.122601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fe2ae558-e91b-489d-b97a-4dcaf3e1a1a6 · outbound

This paper cites Glow: Graph Lowering Compiler Techniques for Neural Networks.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Glow: Graph Lowering Compiler Techniques for Neural Networks

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T04:26:08.031715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c411675-7b1e-4262-a563-dda58b419081 · outbound

This paper cites Ima- genet large scale visual recognition challenge.International journal of computer vision, 115:211–252, 2015.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Ima- genet large scale visual recognition challenge.International journal of computer vision, 115:211–252, 2015

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:08.880410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f71f28d4-8a4b-4f7a-94f2-f96ca99e5193 · outbound

This paper cites Dirty road can attack: Security of deep learning based automated lane centering under{Physical-World} attack.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Dirty road can attack: Security of deep learning based automated lane centering under{Physical-World} attack

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:08.526562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f070cee2-4799-4789-a5aa-678887e812b6 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T04:26:08.046005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a971b27-31d5-44c8-99ba-1e27a634d335 · outbound

This paper cites Just-in-time code reuse: On the effectiveness of fine-grained address space layout randomization.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Just-in-time code reuse: On the effectiveness of fine-grained address space layout randomization

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:08.396341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8209aa55-1edf-47f6-91f0-1f12cf342342 · outbound

This paper cites Aegis: Mitigating targeted bit-flip attacks against deep neural networks.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Aegis: Mitigating targeted bit-flip attacks against deep neural networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:08.323315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 70b369ab-d605-429d-9c1e-0853d25f8884 · outbound

This paper cites Scalable and secure row-swap: Efficient and safe row hammer mitigation in memory systems.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Scalable and secure row-swap: Efficient and safe row hammer mitigation in memory systems

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:08.292953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 644a9a18-9953-4752-9f6e-75a4dd23fe54 · outbound

This paper cites In29th USENIX Security Symposium (USENIX Security 20), pages 1463–1480, 2020.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks In29th USENIX Security Symposium (USENIX Security 20), pages 1463–1480, 2020

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:08.277369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f393fbfc-d67b-4d15-be84-72690be51672 · outbound

This paper cites Optimizing federated learning in distributed industrial iot: A multi-agent approach.IEEE Journal on Selected Areas in Communications, 39(12):3688–3703, 2021.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Optimizing federated learning in distributed industrial iot: A multi-agent approach.IEEE Journal on Selected Areas in Communications, 39(12):3688–3703, 2021

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:08.261907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1646194e-d5f7-460e-8784-3fff1b08d8a2 · outbound

This paper cites Obfunas: A neural architecture search- based dnn obfuscation approach.

ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks Obfunas: A neural architecture search- based dnn obfuscation approach

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:26:08.247579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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