From f32b6bb3618aa3fdd0e300a542840bebdd9352dc Mon Sep 17 00:00:00 2001 From: David Evans Date: Fri, 22 Nov 2019 22:40:32 -0500 Subject: [PATCH] Rebuilt site --- content/concentration.md | 2 +- content/costsensitive.md | 39 ++++ content/docs/cost-sensitive-poster.pdf | Bin 0 -> 668480 bytes content/images/cost-sensitive-cifar.png | Bin 0 -> 346276 bytes .../images/cost-sensitive-poster-small.png | Bin 0 -> 1104406 bytes content/images/protecteven.png | Bin 0 -> 339640 bytes content/main.md | 42 +++- public/concentration/index.html | 2 +- public/costsensitive/index.html | 193 ++++++++++++++++++ public/docs/cost-sensitive-poster.pdf | Bin 0 -> 668480 bytes public/images/cost-sensitive-cifar.png | Bin 0 -> 346276 bytes public/images/cost-sensitive-poster-small.png | Bin 0 -> 1104406 bytes public/images/protecteven.png | Bin 0 -> 339640 bytes public/index.html | 44 +++- public/index.xml | 90 +++++++- public/main/index.html | 42 +++- public/sitemap.xml | 4 + 17 files changed, 431 insertions(+), 27 deletions(-) create mode 100644 content/costsensitive.md create mode 100644 content/docs/cost-sensitive-poster.pdf create mode 100644 content/images/cost-sensitive-cifar.png create mode 100644 content/images/cost-sensitive-poster-small.png create mode 100644 content/images/protecteven.png create mode 100644 public/costsensitive/index.html create mode 100644 public/docs/cost-sensitive-poster.pdf create mode 100644 public/images/cost-sensitive-cifar.png create mode 100644 public/images/cost-sensitive-poster-small.png create mode 100644 public/images/protecteven.png diff --git a/content/concentration.md b/content/concentration.md index e1a0d11..d2b97ad 100644 --- a/content/concentration.md +++ b/content/concentration.md @@ -7,7 +7,7 @@ title = "Empirically Measuring Concentration" Recent theoretical results, starting with Gilmer et al.'s [_Adversarial Spheres_](https://aipavilion.github.io/) (2018), show that if inputs are drawn from a concentrated metric probability space, -then adversarial examples with small perturbation are inevitable. The +then adversarial examples with small perturbation are inevitable.c The key insight from this line of research is that [_concentration of measure_](https://en.wikipedia.org/wiki/Concentration_of_measure">) gives lower bound on adversarial risk for a large collection of diff --git a/content/costsensitive.md b/content/costsensitive.md new file mode 100644 index 0000000..beae2e9 --- /dev/null +++ b/content/costsensitive.md @@ -0,0 +1,39 @@ ++++ +title = "Cost-Sensitive Robustness" ++++ + +Several recent works have developed methods for training classifiers +that are certifiably robust against norm-bounded adversarial +perturbations. However, these methods assume that all the adversarial +transformations provide equal value for adversaries, which is seldom +the case in real-world applications. + +We advocate for cost-sensitive robustness as the criteria for +measuring the classifier's performance for specific tasks. We encode +the potential harm of different adversarial transformations in a cost +matrix, and propose a general objective function to adapt the robust +training method of Wong & Kolter (2018) to optimize for cost-sensitive +robustness. Our experiments on simple MNIST and CIFAR10 models and a +variety of cost matrices show that the proposed approach can produce +models with substantially reduced cost-sensitive robust error, while +maintaining classification accuracy. + +
+ +
+This shows the results of cost-sensitive robustness training to protect the odd classes. By incorporating a cost matrix in the loss function for robustness training, we can produce a model where selected transitions are more robust to adversarial transformation. +
+ +
+ +
+ +### Paper + +Xiao Zhang and David Evans. [_Cost-Sensitive Robustness against Adversarial Examples_](/docs/cost-sensitive-robustness.pdf). In Seventh International Conference on Learning Representations (ICLR). New Orleans. May 2019. [arXiv] [OpenReview] [PDF] + +### Code + +[_https://github.com/xiaozhanguva/Cost-Sensitive-Robustness_](https://github.com/xiaozhanguva/Cost-Sensitive-Robustness) diff --git a/content/docs/cost-sensitive-poster.pdf b/content/docs/cost-sensitive-poster.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2ad3f01ac4e0322ec168d9d2526ca0f273baeb97 GIT binary patch literal 668480 zcmeFYWmH^Iwk;goEm+|N1cFnzySux)OK>M>aCZyt7CgASySuvtw~ut+9=Gp%_xJby zbk(SR>g=<&tXcbvT6@iT$mE4ZX&LBP;K@4fbKl^ZfFPiqp(Q*wH;`W1!q&vV!NSAD z7|8H(24rPrXJ!Wh>BWIsKz23|1CWKC9jF7Omjki`=|w&+v$1h-0O@7_S}XsxX5e52 z^76u)*c$)s68Qh;3!eGk98q?+Hv!VCE1H;oxM5;z=41|J0kHzBB0A%|3 ze83=KYi#27QNaXcVrBeig{j@gB@j>%NUvyT=k(FVKfWt{K>A_x&kkMw2^8D^2@bu4 zFi?xzm<`0jz`|y1z`$(G%D@3)VqjrsV>4tnVmCHrFk)up=ScZhIyLJbEnqbk;UJp!PCrj3eI~6Mi8b3z!h2@8NRk$+N+TXio7dGPem%jkMMnCz+ujE_Z2 zV7_%+go@UOC*R`MJFE2t?0qs}b-LV{MNwa4P~zMqQZ1UK?%|3^&aI2tL!ysVJFr>` zaQu0|&BlP)3b$_jFzPcK&>L_1)LavCr0Dsy8h8|U7Q=cF@5{KPQW%~}XX5)LD 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    @@ -40,15 +40,45 @@ and sophisticated adversaries.
    - Genetic Search
    -Evolutionary framework to automatically find variants that preserve malicious behavior but evade a target classifier. + Empirically Measuring Concentration
    +Method to empirically +measure concentration of real datasets, finding that it does not +explain the lack of robustness of state-of-the-art models.

    +
    -
    + +
    Feature Squeezing
    -Reducing the search space for adversaries by coalescing inputs.
    -(The top row shows L0 adversarial examples, squeezed by median smoothing.) +Reduce search space for adversaries by coalescing inputs. (Top row shows $\ell_0$ adversarial examples, squeezed by median smoothing.)
    + +
    + + +
    + +
    + + +
    +
    + +
    +
    Cost-Senstivie Robustness
    + +
    + +
    +
    + Genetic Search
    +Evolutionary framework to automatically find variants that preserve malicious behavior but evade a target classifier. +
    +
    diff --git a/public/concentration/index.html b/public/concentration/index.html index 7628a0c..860b542 100644 --- a/public/concentration/index.html +++ b/public/concentration/index.html @@ -134,7 +134,7 @@

    Estimating the

    Recent theoretical results, starting with Gilmer et al.’s Adversarial Spheres (2018), show that if inputs are drawn from a concentrated metric probability space, -then adversarial examples with small perturbation are inevitable. The +then adversarial examples with small perturbation are inevitable.c The key insight from this line of research is that concentration of measure gives lower bound on adversarial risk for a large collection of diff --git a/public/costsensitive/index.html b/public/costsensitive/index.html new file mode 100644 index 0000000..1adc3cc --- /dev/null +++ b/public/costsensitive/index.html @@ -0,0 +1,193 @@ + + + + + Cost-Sensitive Robustness // EvadeML + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    + + + + + +
    + + + + +
    +

    + Cost-Sensitive Robustness +

    +

    + +

    + + + + + + + + + + + + + +

    Several recent works have developed methods for training classifiers +that are certifiably robust against norm-bounded adversarial +perturbations. However, these methods assume that all the adversarial +transformations provide equal value for adversaries, which is seldom +the case in real-world applications.

    + +

    We advocate for cost-sensitive robustness as the criteria for +measuring the classifier’s performance for specific tasks. We encode +the potential harm of different adversarial transformations in a cost +matrix, and propose a general objective function to adapt the robust +training method of Wong & Kolter (2018) to optimize for cost-sensitive +robustness. Our experiments on simple MNIST and CIFAR10 models and a +variety of cost matrices show that the proposed approach can produce +models with substantially reduced cost-sensitive robust error, while +maintaining classification accuracy.

    + +

    + +
    +This shows the results of cost-sensitive robustness training to protect the odd classes. By incorporating a cost matrix in the loss function for robustness training, we can produce a model where selected transitions are more robust to adversarial transformation. +

    + +

    + +

    + +

    Paper

    + +

    Xiao Zhang and David Evans. Cost-Sensitive Robustness against Adversarial Examples. In Seventh International Conference on Learning Representations (ICLR). New Orleans. May 2019. [arXiv] [OpenReview] [PDF]

    + +

    Code

    + +

    https://github.com/xiaozhanguva/Cost-Sensitive-Robustness

    + + + + + + +
    + + + + + + +
    +
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      Is Robust Machine Learning Possible?

      @@ -146,7 +148,7 @@

      Projects

      @@ -155,15 +157,45 @@

      Projects

      - Genetic Search
      -Evolutionary framework to automatically find variants that preserve malicious behavior but evade a target classifier. + Empirically Measuring Concentration
      +Method to empirically +measure concentration of real datasets, finding that it does not +explain the lack of robustness of state-of-the-art models.

      +
      -
      + +
      Feature Squeezing
      -Reducing the search space for adversaries by coalescing inputs.
      -(The top row shows L0 adversarial examples, squeezed by median smoothing.) +Reduce search space for adversaries by coalescing inputs. (Top row shows $\ell_0$ adversarial examples, squeezed by median smoothing.)
      + +
      + + +
      + +
      + + +
      +
      + +
      +
      Cost-Senstivie Robustness
      + +
      + +
      +
      + Genetic Search
      +Evolutionary framework to automatically find variants that preserve malicious behavior but evade a target classifier. +
      +
      diff --git a/public/index.xml b/public/index.xml index 511a974..e25645d 100644 --- a/public/index.xml +++ b/public/index.xml @@ -86,6 +86,52 @@ Helen Simecek (Undergraduate Researcher)</p> + + Cost-Sensitive Robustness + //evademl.org/costsensitive/ + Mon, 01 Jan 0001 00:00:00 +0000 + + //evademl.org/costsensitive/ + + +<p>Several recent works have developed methods for training classifiers +that are certifiably robust against norm-bounded adversarial +perturbations. However, these methods assume that all the adversarial +transformations provide equal value for adversaries, which is seldom +the case in real-world applications.</p> + +<p>We advocate for cost-sensitive robustness as the criteria for +measuring the classifier&rsquo;s performance for specific tasks. We encode +the potential harm of different adversarial transformations in a cost +matrix, and propose a general objective function to adapt the robust +training method of Wong &amp; Kolter (2018) to optimize for cost-sensitive +robustness. Our experiments on simple MNIST and CIFAR10 models and a +variety of cost matrices show that the proposed approach can produce +models with substantially reduced cost-sensitive robust error, while +maintaining classification accuracy.</p> + +<p><center> +<img src="//evademl.org/images/protecteven.png" width="70%"> +<div class="caption" align="left" style="padding-left:5rem;padding-right:5rem"> +This shows the results of cost-sensitive robustness training to protect the odd classes. By incorporating a cost matrix in the loss function for robustness training, we can produce a model where selected transitions are more robust to adversarial transformation. +</center></p> + +<p><center> +<a href="//evademl.org/docs/cost-sensitive-poster.pdf"><img src="//evademl.org/images/cost-sensitive-poster-small.png" width="90%" align="center"></a> +</center></p> + +<h3 id="paper">Paper</h3> + +<p>Xiao Zhang and David Evans. <a href="//evademl.org/docs/cost-sensitive-robustness.pdf"><em>Cost-Sensitive Robustness against Adversarial Examples</em></a>. In <a +href="https://iclr.cc/Conferences/2019"><em>Seventh International Conference on Learning Representations</em></a> (ICLR). New Orleans. May 2019. [<a href="https://arxiv.org/abs/1810.09225">arXiv</a>] [<a +href="https://openreview.net/forum?id=BygANhA9tQ">OpenReview</a>] [<a href="//evademl.org/docs/cost-sensitive-robustness.pdf">PDF</a>]</p> + +<h3 id="code">Code</h3> + +<p><a href="https://github.com/xiaozhanguva/Cost-Sensitive-Robustness"><em>https://github.com/xiaozhanguva/Cost-Sensitive-Robustness</em></a></p> + + + Empirically Measuring Concentration //evademl.org/concentration/ @@ -99,7 +145,7 @@ Helen Simecek (Undergraduate Researcher)</p> <p>Recent theoretical results, starting with Gilmer et al.&rsquo;s <a href="https://aipavilion.github.io/"><em>Adversarial Spheres</em></a> (2018), show that if inputs are drawn from a concentrated metric probability space, -then adversarial examples with small perturbation are inevitable. The +then adversarial examples with small perturbation are inevitable.c The key insight from this line of research is that <a href="https://en.wikipedia.org/wiki/Concentration_of_measure&quot;"><em>concentration of measure</em></a> gives lower bound on adversarial risk for a large collection of @@ -222,7 +268,7 @@ and sophisticated adversaries.</p> <section style="display: table;width: 100%"> <header style="display: table-row; padding: 0.5rem"> <div style="display: table-cell; padding: 0.5rem; color:#FFFFFF;background:#663399;text-align: center;width: 49%"> -<a href="//evademl.org/gpevasion" class="hlink">Genetic&nbsp;Programming</a> +<a href="//evademl.org/concentration" class="hlink">Measuring Concentration</a> </div> <div style="display: table-cell; padding: 0.5rem;color:#000000;background: #FFFFFF;text-align: center; width:2%""></div> <div style="display: table-cell; padding: 0.5rem;color:#FFFFFF;background: #2c0f52;text-align: center;"> @@ -231,15 +277,45 @@ and sophisticated adversaries.</p> </header> <div style="display: table-row;"> <div style="display: table-cell;"> - <a href="//evademl.org/gpevasion"><img src="//evademl.org/images/geneticsearch.png" alt="Genetic Search" width="100%" align="center"></a><br> -Evolutionary framework to automatically find variants that preserve malicious behavior but evade a target classifier. + <a href="//evademl.org/concentration"><img src="//evademl.org/images/concentration/alg.png" alt="Empirically Measuring Concentration" width="100%" align="center"></a><br> +Method to empirically +measure concentration of real datasets, finding that it does not +explain the lack of robustness of state-of-the-art models.<br></br> </div> + <div style="display: table-cell;"></div> - <div style="display: table-cell;text-align:center"> + + <div style="display: table-cell;text-align:left;"> <a href="//evademl.org/squeezing"><img src="//evademl.org/images/squeezing.png" alt="Feature Squeezing" width="100%" align="center"></a><br> -Reducing the search space for adversaries by coalescing inputs.<br> -<font size="-1" style="color:#666;">(The top row shows L<sub>0</sub> adversarial examples, squeezed by median smoothing.)</font> +Reduce search space for adversaries by coalescing inputs. <font style="color:#666;line-spacing:0.5;" size="-1">(Top row shows $\ell_0$ adversarial examples, squeezed by median smoothing.)</font> </div> + + </div> + + + <header style="display: table-row; padding: 0.5rem"> + <div style="display: table-cell; padding: 0.5rem;color:#FFFFFF;background: #2c0f52;text-align: center;"> +<a href="//evademl.org/costsensitive" class="hlink">Cost-Sensitive Robustness</a> + </div> + <div style="display: table-cell; padding: 0.5rem;color:#000000;background: #FFFFFF;text-align: center; width:2%""></div> + <div style="display: table-cell; padding: 0.5rem; color:#FFFFFF;background:#663399;text-align: center;width: 49%"> +<a href="//evademl.org/gpevasion" class="hlink">Genetic&nbsp;Programming</a> + </div> + + </header> + <div style="display: table-row;"> + + <div style="display: table-cell;padding:0"> + <center><a href="//evademl.org/costsensitive"><img src="//evademl.org/images/cost-sensitive-cifar.png" alt="Cost-Senstivie Robustness" width="80%" align="center"></a></center> +<!--Focus robust training on transitions given by a cost matrix to make security-critical transitions robust.--> + </div> + + <div style="display: table-cell;"></div> + <div style="display: table-cell;"> + <a href="//evademl.org/gpevasion"><img src="//evademl.org/images/geneticsearch.png" alt="Genetic Search" width="100%" align="center"></a><br> +Evolutionary framework to automatically find variants that preserve malicious behavior but evade a target classifier. + </div> + </div> </section> diff --git a/public/main/index.html b/public/main/index.html index 7d0d7d1..f94c206 100644 --- a/public/main/index.html +++ b/public/main/index.html @@ -157,7 +157,7 @@

      Projects

      @@ -166,15 +166,45 @@

      Projects

      - Genetic Search
      -Evolutionary framework to automatically find variants that preserve malicious behavior but evade a target classifier. + Empirically Measuring Concentration
      +Method to empirically +measure concentration of real datasets, finding that it does not +explain the lack of robustness of state-of-the-art models.

      +
      -
      + +
      Feature Squeezing
      -Reducing the search space for adversaries by coalescing inputs.
      -(The top row shows L0 adversarial examples, squeezed by median smoothing.) +Reduce search space for adversaries by coalescing inputs. (Top row shows $\ell_0$ adversarial examples, squeezed by median smoothing.)
      + +
      + + +
      + +
      + + +
      +
      + +
      +
      Cost-Senstivie Robustness
      + +
      + +
      +
      + Genetic Search
      +Evolutionary framework to automatically find variants that preserve malicious behavior but evade a target classifier. +
      +
      diff --git a/public/sitemap.xml b/public/sitemap.xml index 7bcfe33..38d384d 100644 --- a/public/sitemap.xml +++ b/public/sitemap.xml @@ -13,6 +13,10 @@ //evademl.org/gpevasion/ + + //evademl.org/costsensitive/ + + //evademl.org/concentration/

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