Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:19:55.972977Z
Paper Citation Record · LEDGER
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems
As of 16 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:1908.03369.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
52 of 52 outbound references displayed
- verified exact0
- verified fuzzy47
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation ba4e55f4-9ebe-4461-9971-6243d26b894a · outbound
This paper cites Deepdriving: Learning affordance for direct perception in autonomous driving,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Deepdriving: Learning affordance for direct perception in autonomous driving,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.931665Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2b346346-1807-4507-b3ac-4da660c6f12f · outbound
This paper cites Medical image analysis using convolutional neural networks: a review,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Medical image analysis using convolutional neural networks: a review,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.919924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5d887025-aa1f-4f24-984d-fc322fc2e55e · outbound
This paper cites Deepface: Closing the gap to human-level performance in face verification,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Deepface: Closing the gap to human-level performance in face verification,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.908077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8d30b422-c720-4807-8b44-7d34341d40ed · outbound
This paper cites Adversary resistant deep neural networks with an application to malware detection,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Adversary resistant deep neural networks with an application to malware detection,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.894975Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a9141273-9a4b-4730-ab8a-3b82502726da · outbound
This paper cites Droid-sec: deep learning in android malware detection,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Droid-sec: deep learning in android malware detection,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.881535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7e154007-fc81-475f-a5f0-8f2ac2a18464 · outbound
This paper cites Badnets: Evaluating backdooring attacks on deep neural networks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Badnets: Evaluating backdooring attacks on deep neural networks,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.867894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 14cc8b56-29ed-47a2-a892-d76456165d40 · outbound
This paper cites Amazon machine learning.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Amazon machine learning
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.853171Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ff16fb0d-22c9-452c-ab3b-139ba955746c · outbound
This paper cites Caffe model zoo.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Caffe model zoo
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.839360Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3a964839-a1af-43b9-8872-6b36755a5c4d · outbound
This paper cites Gradientzoo: pre-trained neural network models.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Gradientzoo: pre-trained neural network models
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.824817Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 52cedf56-c6ad-44c1-bf74-0536b29fd396 · outbound
This paper cites Model zoo.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Model zoo
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.810056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 320ec28d-c189-4404-a7f4-c15162850f63 · outbound
This paper cites Trojaning attack on neural networks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Trojaning attack on neural networks,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.795573Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a8f445f4-b8a2-4665-b589-8e472064562c · outbound
This paper cites Targeted backdoor attacks on deep learning systems using data poisoning,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Targeted backdoor attacks on deep learning systems using data poisoning,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.778942Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cf6066ef-4141-4216-964a-b08f515e2cb3 · outbound
This paper cites How to backdoor federated learning,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems How to backdoor federated learning,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.764146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 507844a9-4106-4e31-94a3-409fb9948114 · outbound
This paper cites Evaluating the visualization of what a deep neural network has learned,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Evaluating the visualization of what a deep neural network has learned,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.749689Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4fa2ce2e-fec3-44f0-a833-e415cd1fbaa7 · outbound
This paper cites The challenges and opportunities of explainable ai,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems The challenges and opportunities of explainable ai,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.734104Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5eb00b16-f52e-4f58-bfd1-179c87f48784 · outbound
This paper cites Broad agency announcement for trojai.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Broad agency announcement for trojai
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.720216Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation afeca122-3277-4dd4-874b-472db49d0d64 · outbound
This paper cites Sentinet: Detecting physical attacks against deep learning systems,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Sentinet: Detecting physical attacks against deep learning systems,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.707000Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 537c2172-6f89-4d78-8816-1497b2f5d60b · outbound
This paper cites Strip: A defence against trojan attacks on deep neural networks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Strip: A defence against trojan attacks on deep neural networks,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.690272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7e9a0122-77c6-4fb3-87b6-c10c8c81e71b · outbound
This paper cites Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.671890Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0aff7b61-364f-446d-9eee-110f67a6dd90 · outbound
This paper cites Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.655925Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8a625fb0-c8c3-48ad-bb19-87b6bfbb04b6 · outbound
This paper cites Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.639995Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e23173a9-a8d2-481d-993d-317414055ba0 · outbound
This paper cites Fine-pruning: Defending against backdooring attacks on deep neural networks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Fine-pruning: Defending against backdooring attacks on deep neural networks,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.626109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1627980f-a7da-4ced-a6b1-38d730ca0ef3 · outbound
This paper cites Visualizing data using t-SNE,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Visualizing data using t-SNE,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.608055Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation eb07366a-8af7-4009-b74b-6b17d7a96a8e · outbound
This paper cites Intriguing properties of neural networks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Intriguing properties of neural networks,
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:19:55.842159Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9167a456-cc18-4873-a808-2cc3ed9daff4 · outbound
This paper cites ABS: Scanning neural networks for back-doors by artificial brain stimula- tion,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems ABS: Scanning neural networks for back-doors by artificial brain stimula- tion,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.581084Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8ed42c37-c64d-46c2-8e8f-3f54f9fcbff3 · outbound
This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Grad-cam: Visual explanations from deep networks via gradient-based localization,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.563237Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e15210b0-1ca0-414a-b574-58861e9bfdb1 · outbound
This paper cites Generative adversarial nets,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Generative adversarial nets,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.547896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 09325915-5098-42c8-a62f-1acca61fb789 · outbound
This paper cites Globally and locally consistent image completion,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Globally and locally consistent image completion,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.534026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3360218d-7d80-40c9-ae6e-b4972bdaf981 · outbound
This paper cites Improved training of wasserstein gans,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Improved training of wasserstein gans,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.518515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 95a4d28e-0aa8-4e4a-a977-827bff541f73 · outbound
This paper cites Learning multiple layers of features from tiny images,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Learning multiple layers of features from tiny images,
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:19:55.869036Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d3d8c2a4-28db-427f-99c7-c900e564cd81 · outbound
This paper cites Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.492027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cb2f26c8-8710-4d44-8ae3-dbc24f4d8d6c · outbound
This paper cites Traffic sign recognition how far are we from the solution?.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Traffic sign recognition how far are we from the solution?
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.472541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 84d58bde-51f1-45e4-b41b-da244a5016ee · outbound
This paper cites Vggface2: A dataset for recognising faces across pose and age,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Vggface2: A dataset for recognising faces across pose and age,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.454304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fe8acefe-30a0-409f-bea1-3adc18388039 · outbound
This paper cites Very deep convolutional networks for large-scale image recognition,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Very deep convolutional networks for large-scale image recognition,
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:19:55.887121Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03df75d2-e843-4ea9-a473-3fdbd0dc32f2 · outbound
This paper cites Deep residual learning for image recognition,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Deep residual learning for image recognition,
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:19:55.891345Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1cde6e2d-9720-49b4-92f5-5eb54d446016 · outbound
This paper cites Deep face recognition,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Deep face recognition,
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.420211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation dde62024-cf14-4d5d-9ebb-6e78d0abdc05 · outbound
This paper cites Interpretable deep learning under fire,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Interpretable deep learning under fire,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.405838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d9950bc9-350e-4f02-9035-20873f5788c8 · outbound
This paper cites Explaining and harness- ing adversarial examples,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Explaining and harness- ing adversarial examples,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.391614Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 046f1a48-efa1-43c7-b785-a416b662bb37 · outbound
This paper cites Towards deep learning models resistant to adversarial attacks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Towards deep learning models resistant to adversarial attacks,
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:19:55.909263Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9d4c7281-33d7-4741-a544-8c40141f74f5 · outbound
This paper cites Invisible backdoor attacks on deep neural networks via steganography and regularization,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Invisible backdoor attacks on deep neural networks via steganography and regularization,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.365430Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ad11d749-e21b-4a5b-89f1-707223c37d57 · outbound
This paper cites Reflection backdoor: A natural backdoor attack on deep neural networks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Reflection backdoor: A natural backdoor attack on deep neural networks,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.346349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 01af175f-fb03-49a5-961f-6345c72a9aaf · outbound
This paper cites Hidden trigger backdoor attacks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Hidden trigger backdoor attacks,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.329304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ea7c1d89-9685-4032-9990-b7774ddbdef5 · outbound
This paper cites Blind backdoors in deep learning models,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Blind backdoors in deep learning models,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.314150Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 84e6fc08-96ac-471f-9740-9daa1af8470b · outbound
This paper cites Detecting backdoor attacks on deep neural networks by activation clustering,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Detecting backdoor attacks on deep neural networks by activation clustering,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.299113Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c377b8c1-0382-4651-ac55-d968c1390e63 · outbound
This paper cites Design and evaluation of a multi-domain trojan detection method on deep neural networks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Design and evaluation of a multi-domain trojan detection method on deep neural networks,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.281512Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 65780f49-23bf-45c2-b27e-10280994e515 · outbound
This paper cites Neural trojans,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Neural trojans,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.266638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1f179079-5263-499d-95a7-13e91c7187e6 · outbound
This paper cites Backdoor attacks to graph neural networks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Backdoor attacks to graph neural networks,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.250135Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 94685a09-06be-4d0f-b747-13ceacf4ce0b · outbound
This paper cites Rab: Provable robustness against backdoor attacks,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Rab: Provable robustness against backdoor attacks,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.231609Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 19d29d99-bd93-4be9-8157-7ab39ef313e0 · outbound
This paper cites On certifying robustness against backdoor attacks via randomized smoothing,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems On certifying robustness against backdoor attacks via randomized smoothing,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.214912Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation aae74d5f-34b4-4163-9046-7d4423beeff9 · outbound
This paper cites Mnist handwritten digit database,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Mnist handwritten digit database,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.198574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fa3cf8fc-ea37-4393-9ab7-49195219b236 · outbound
This paper cites The architectural implications of autonomous driving: Constraints and acceleration,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems The architectural implications of autonomous driving: Constraints and acceleration,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.178004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fecc4d15-4c25-4fb7-8569-fea4aa5fed53 · outbound
This paper cites Simultaneous traffic sign detection and bound- ary estimation using convolutional neural network,.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Simultaneous traffic sign detection and bound- ary estimation using convolutional neural network,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:19:56.159717Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.