Chaofei Yang, Qing Wu, Hai Li, and Yiran Chen. 2017b.
Generative Poisoning Attack Method Against Neural Networks.
arXiv preprint 1703.01340 (2017). https://arxiv.org/pdf/1703.01340
Generative Poisoning Attack Method Against Neural Networks.
arXiv preprint 1703.01340 (2017). https://arxiv.org/pdf/1703.01340
E. The Loss-based Countermeasure against Poisoning Attack
Moreover, we propose an universal method to detect the
aforementioned poisoning attack methods, which is shown in
Algorithm 3. Once a data (no matter normal or poisoned) is
injected into the target model, the loss of the target model is
recorded. If the loss exceeds the pre-determined threshold Lth,
a warning will show up. If the number of warnings exceeds the
threshold Wth, the accuracy check will be triggered to examine
if a poisoning attack is indeed being conducted. Our method
is based on the phenomenon that the poisoned inp
Moreover, we propose an universal method to detect the
aforementioned poisoning attack methods, which is shown in
Algorithm 3. Once a data (no matter normal or poisoned) is
injected into the target model, the loss of the target model is
recorded. If the loss exceeds the pre-determined threshold Lth,
a warning will show up. If the number of warnings exceeds the
threshold Wth, the accuracy check will be triggered to examine
if a poisoning attack is indeed being conducted. Our method
is based on the phenomenon that the poisoned inp
nput and label
pair usually results in a larger loss, compared with the normal
one. This is naturally understandable as the goal of poisoning
attack is to minimize the model accuracy, which is realized
through maximizing the loss. The poisoned data alters target
model’s original decision boundary. On the contrary, normal
data stays inside the decision region (at least most of time),
inducing a relatively smaller loss. We are able to monitor the
condition of inputs by checking the loss periodically.
pair usually results in a larger loss, compared with the normal
one. This is naturally understandable as the goal of poisoning
attack is to minimize the model accuracy, which is realized
through maximizing the loss. The poisoned data alters target
model’s original decision boundary. On the contrary, normal
data stays inside the decision region (at least most of time),
inducing a relatively smaller loss. We are able to monitor the
condition of inputs by checking the loss periodically.
H. Xiao, H. Xiao, and C. Eckert. Adversarial label flips attack on support
vector machines. European Conference on Artificial Intelligence, pages
870–875, 2012.
vector machines. European Conference on Artificial Intelligence, pages
870–875, 2012.
This sound like peak health right here (sarcasm) https://www.facebook.com/friendsofcoalky/posts/water-draining-from-a-worked-out-mine-can-be-cleaned-and-mined-at-the-same-time-/1420376790197884/
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Water draining from a worked-out mine can be cleaned and mined at the same time. West Virginia University pipes it through a hillside plant, where it runs through three pools and comes out nearly...
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Update on that bit from Niger - how the Pres/P.M. said they were about to be attacked b y USA, Izreal, France and Ukraine connected forces?
Closing thought on tonights show I was to share when I lost my train of thought - "Is there an obligation in the CFAA that supports the idea that there's a duty to only publish clean data. Realistically there's no way to know if your fanfic is going to 'damage' an LLM that then spits out incorrect facts about the real books. There's no way to know that your Wiley E Coyote style murel is going to confuse a self driving car trying to map your neighborhood. There is no obligation in the CFAA to build a world that isn't hostile to the passive consumption of other people's data.
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^^^Random Reddit Guy in response to the Data/image Poisoning is criminal argument
Poisoning Web-Scale Training Datasets is Practical - arXiv:2302.10149v2 [cs.CR] 06 May 2024
DOI:10.1109/JBHI.2014.2344095Corpus ID: 18466660
Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare
Mehran Mozaffari-Kermani, Susmita Sur-Kolay, +1 author N. Jha
Published in IEEE journal of biomedical… 1 November 2015
Computer Science, Medicine
Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare
Mehran Mozaffari-Kermani, Susmita Sur-Kolay, +1 author N. Jha
Published in IEEE journal of biomedical… 1 November 2015
Computer Science, Medicine