Spaces:
Running
Running
File size: 26,149 Bytes
e83d3d6 4d22a83 e83d3d6 4d22a83 e83d3d6 1a23e33 e83d3d6 caaf80c e83d3d6 344407b 93ef4d5 195942f 1a87ac9 93ef4d5 f6cc227 d17ac5b 50238de f6cc227 4817884 a0a9740 f6cc227 75fab0b ec73a31 c5fd5e5 ec73a31 c5fd5e5 ec73a31 d17ac5b f6cc227 344407b e83d3d6 0e03fed e83d3d6 4d22a83 e83d3d6 5366905 e83d3d6 5366905 e83d3d6 5366905 e83d3d6 4d22a83 e83d3d6 4d22a83 e83d3d6 5366905 e83d3d6 5366905 e83d3d6 5366905 e83d3d6 5366905 e83d3d6 5366905 e83d3d6 5366905 e83d3d6 4d22a83 5366905 e83d3d6 ae66a58 b19c065 ae66a58 82fd2c3 ae66a58 eade1a5 82fd2c3 ae66a58 b19c065 e83d3d6 b19c065 f4a2536 82fd2c3 f4a2536 82fd2c3 f4a2536 82fd2c3 f4a2536 82fd2c3 f4a2536 344407b b1be04f 42d4e62 75fab0b b19c065 b1be04f e83d3d6 4d22a83 5366905 e83d3d6 42805da e83d3d6 42805da e83d3d6 0e03fed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 |
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="Demo Page of GREAT Score Neurips 2024.">
<meta name="keywords" content="GREAT Score, Adversarial robustness, Generative models">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>GREAT Score: Global Robustness Evaluation of
Adversarial Perturbation using Generative Models</title>
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
rel="stylesheet">
<link rel="stylesheet" href="./static/css/bulma.min.css">
<link rel="stylesheet" href="./static/css/bulma-carousel.min.css">
<link rel="stylesheet" href="./static/css/bulma-slider.min.css">
<link rel="stylesheet" href="./static/css/fontawesome.all.min.css">
<link rel="stylesheet"
href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
<link rel="stylesheet" href="./static/css/index.css">
<link rel="stylesheet" href="./static/css/custom.css">
<link rel="icon" href="./static/images/favicon.svg">
<!-- <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script> -->
<script src="https://code.jquery.com/jquery-3.6.0.js"></script>
<script src="https://code.jquery.com/ui/1.13.2/jquery-ui.js"></script>
<script defer src="./static/js/fontawesome.all.min.js"></script>
<script src="./static/js/bulma-carousel.min.js"></script>
<script src="./static/js/bulma-slider.min.js"></script>
<script src="./static/js/index.js"></script>
<!-- for mathjax support -->
<!-- <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script> -->
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
<script>
$(document).ready(function(){
$('#adaptive-loss-formula-list').on('click', 'a', function(e) {
e.preventDefault();
if (!$(this).hasClass('selected')) {
$('.formula-content').hide(200);
$('.formula-list > a').removeClass('selected');
$(this).addClass('selected');
var target = $(this).attr('href');
$(target).show(200);
}
});
$('#adaptive-dataset').on('click', 'a', function(e) {
e.preventDefault();
if (!$(this).hasClass('selected')) {
$('.interpolation-video-column').hide();
$('#adaptive-dataset > a').removeClass('selected');
$(this).addClass('selected');
var target = $(this).attr('href');
$(target).show();
}
});
})
</script>
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-baqh{text-align:center;vertical-align:top}
.tg .tg-amwm{font-weight:bold;text-align:center;vertical-align:top}
.tg .tg-2imo{font-style:italic;text-align:center;text-decoration:underline;vertical-align:top}
</style>
</head>
<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">GREAT Score: Global Robustness Evaluation of
Adversarial Perturbation using Generative Models</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="#" target="_blank">ZAITANG LI</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://sites.google.com/site/pinyuchenpage/home" target="_blank">Pin-Yu Chen</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://tsungyiho.github.io/" target="_blank">Tsung-Yi Ho</a><sup>1</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>The Chinese University of Hong Kong,</span>
<span class="author-block"><sup>2</sup>IBM Research</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://arxiv.org/abs/2304.09875" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a href="https://arxiv.org/abs/2304.09875" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Video Link. -->
<!-- <span class="link-block">
<a href="https://www.youtube.com/watch?v=MrKrnHhk8IA" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-youtube"></i>
</span>
<span>Video</span>
</a>
</span> -->
<!-- Code Link. -->
<!-- <span class="link-block">
<a href="https://github.com/google/nerfies" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span> -->
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- <section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<video id="teaser" autoplay muted loop playsinline height="100%">
<source src="./static/videos/teaser.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
<span class="dnerf">Nerfies</span> turns selfie videos from your phone into
free-viewpoint
portraits.
</h2>
</div>
</div>
</section> -->
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Current studies on adversarial robustness mainly focus on aggregating <i>local</i> robustness results from a set of data samples to evaluate and rank different models. However, the local statistics may not well represent the true <i>global</i> robustness of the underlying unknown data distribution. To address this challenge, this paper makes the first attempt to present a new framework, called <strong>GREAT Score</strong>, for global robustness evaluation of adversarial perturbation using generative models. Formally, GREAT Score carries the physical meaning of a global statistic capturing a mean certified attack-proof perturbation level over all samples drawn from a generative model. For finite-sample evaluation, we also derive a probabilistic guarantee on the sample complexity and the difference between the sample mean and the true mean. GREAT Score has several advantages: (1) Robustness evaluations using GREAT Score are efficient and scalable to large models, by sparing the need of running adversarial attacks. In particular, we show high correlation and significantly reduced computation cost of GREAT Score when compared to the attack-based model ranking on RobustBench<sup>1</sup>. (2) The use of generative models facilitates the approximation of the unknown data distribution. In our ablation study with different generative adversarial networks (GANs), we observe consistency between global robustness evaluation and the quality of GANs. (3) GREAT Score can be used for remote auditing of privacy-sensitive black-box models, as demonstrated by our robustness evaluation on several online facial recognition services.
</p>
</div>
<!-- References -->
<div class="content">
<p>
<sup>1</sup> Croce, F., Andriushchenko, M., Sehwag, V., Debenedetti, E., Flammarion, N., Chiang, M., Mittal, P., & Hein, M. (2021). RobustBench: a standardized adversarial robustness benchmark. In <i>Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)</i>. <a href="https://openreview.net/forum?id=SSKZPJCt7B" target="_blank">https://openreview.net/forum?id=SSKZPJCt7B</a>
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
</div>
</section>
<!-- Overview -->
<section class="section">
<div class="container is-max-desktop">
<h2 class="title is-3">Method Overview of GREAT Score</h2>
<div class="columns is-centered">
<div class="column container-centered">
<img src="./static/images/GREAT_Score_overview.png" alt="Method Overview of GREAT Score"/>
<p><strong>Figure 1. Overview of GREAT Score.</strong> The process involves three main steps:
(1) Data Generation: We use a generative model to create synthetic samples.
(2) Local Robustness Evaluation: For each generated sample, we calculate a local robustness score based on the classifier's confidence.
(3) Global Robustness Estimation: We aggregate the local scores to estimate the overall robustness of the classifier.
This method provides a certified lower bound on the true global robustness without requiring access to the original dataset or exhaustive adversarial attacks.</p>
</div>
</div>
</div>
</section>
<!-- Overview -->
<!-- Robustness Certificate Definition -->
<section class="section">
<div class="container is-max-desktop">
<h2 class="title is-3">Robustness Certificate Definition</h2>
<div class="columns is-centered">
<div class="column container formula">
<p>
GREAT Score is designed to evaluate the global robustness of classifiers against adversarial attacks. It uses generative models to estimate a certified lower bound on true global robustness. For a K-way classifier f, we define a local robustness score g(G(z)) for a generated sample G(z), where G is a generator and z is sampled from a standard Gaussian distribution. This score measures the confidence gap between the correct class prediction and the most likely incorrect class. The GREAT Score, defined as the expectation of g(G(z)) over z, provides a certified lower bound on the true global robustness with respect to the data distribution learned by the generative model. This approach allows us to estimate global robustness without knowing the exact data distribution or minimal perturbations for each sample.
</p>
</div>
</div>
<div class="columns is-centered">
<div class="column container-centered">
<div id="adaptive-loss-formula" class="container">
<div id="adaptive-loss-formula-list" class="row align-items-center formula-list">
<a href=".true-global-robustness" class="selected">True Global Robustness</a>
<a href=".global-robustness-estimate">Global Robustness Estimate</a>
<a href=".local-robustness-score">Local Robustness Score</a>
<div style="clear: both"></div>
</div>
<div class="row align-items-center adaptive-loss-formula-content">
<span class="formula true-global-robustness formula-content">
$$
\displaystyle
\Omega(f) = \mathbb{E}_{x\sim P}[\Delta_{min}(x)]= \int_{x \sim P} \Delta_{\min}(x) p(x)dx
$$
</span>
<span class="formula global-robustness-estimate formula-content" style="display: none;">
$$
\displaystyle
\widehat{\Omega}(f) = \mathbb{E}_{x\sim P}[g(x)]= \int_{x \sim P} g(x) p(x)dx
$$
</span>
<span class="formula local-robustness-score formula-content" style="display: none;">
$$
\displaystyle
g\left(G(z)\right) = \sqrt{\cfrac{\pi}{2}} \cdot \max\{ f_c(G(z)) - \max_{k \in \{1,\ldots,K\},k\neq c} f_k(G(z)),0 \}
$$
</span>
</div>
</div>
</div>
</div>
<div class="columns is-centered">
<div class="column container adaptive-loss-formula-content">
<p class="formula true-global-robustness formula-content">
where f is a classifier, P is a data distribution, and Δ<sub>min</sub>(x) is the minimal perturbation for a sample x.
</p>
<p class="formula global-robustness-estimate formula-content" style="display: none">
where g(x) is a local robustness statistic, and this estimate is used when the exact probability density function of P and local minimal perturbations are unknown.
</p>
<p class="formula local-robustness-score formula-content" style="display: none;">
where G(z) is a generated data sample, f<sub>c</sub> is the confidence score for the correct class c, and f<sub>k</sub> are the confidence scores for other classes.
</p>
</div>
</div>
</section>
<!-- Results -->
<section class="section">
<div class="container is-max-desktop">
<h2 class="title is-3">GREAT Score Results</h2>
<div class="columns is-centered">
<div class="column container-centered">
<table class="tg" border="1" style="width:100%;">
<caption><strong>Table 1.</strong> Comparison of (Calibrated) GREAT Score v.s. minimal distortion found by CW attack on CIFAR-10. The results are averaged over 500 samples from StyleGAN2.</caption>
<thead>
<tr>
<th class="tg-amwm">Model Name</th>
<th class="tg-baqh">RobustBench Accuracy(%)</th>
<th class="tg-baqh">AutoAttack Accuracy(%)</th>
<th class="tg-baqh">GREAT Score</th>
<th class="tg-baqh">Calibrated GREAT Score</th>
<th class="tg-baqh">CW Distortion</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-baqh">Rebuffi_extra</td>
<td class="tg-baqh">82.32</td>
<td class="tg-baqh">87.20</td>
<td class="tg-baqh">0.507</td>
<td class="tg-baqh">1.216</td>
<td class="tg-baqh">1.859</td>
</tr>
<tr>
<td class="tg-baqh">Gowal_extra</td>
<td class="tg-baqh">80.53</td>
<td class="tg-baqh">85.60</td>
<td class="tg-baqh">0.534</td>
<td class="tg-baqh">1.213</td>
<td class="tg-baqh">1.324</td>
</tr>
<tr>
<td class="tg-baqh">Rebuffi_70_ddpm</td>
<td class="tg-baqh">80.42</td>
<td class="tg-baqh">90.60</td>
<td class="tg-baqh">0.451</td>
<td class="tg-baqh">1.208</td>
<td class="tg-baqh">1.943</td>
</tr>
<tr>
<td class="tg-baqh">Rebuffi_28_ddpm</td>
<td class="tg-baqh">78.80</td>
<td class="tg-baqh">90.00</td>
<td class="tg-baqh">0.424</td>
<td class="tg-baqh">1.214</td>
<td class="tg-baqh">1.796</td>
</tr>
<tr>
<td class="tg-baqh">Augustin_WRN_extra</td>
<td class="tg-baqh">78.79</td>
<td class="tg-baqh">86.20</td>
<td class="tg-baqh">0.525</td>
<td class="tg-baqh">1.206</td>
<td class="tg-baqh">1.340</td>
</tr>
<tr>
<td class="tg-baqh">Sehwag</td>
<td class="tg-baqh">77.24</td>
<td class="tg-baqh">89.20</td>
<td class="tg-baqh">0.227</td>
<td class="tg-baqh">1.143</td>
<td class="tg-baqh">1.392</td>
</tr>
<tr>
<td class="tg-baqh">Augustin_WRN</td>
<td class="tg-baqh">76.25</td>
<td class="tg-baqh">86.40</td>
<td class="tg-baqh">0.583</td>
<td class="tg-baqh">1.206</td>
<td class="tg-baqh">1.332</td>
</tr>
<tr>
<td class="tg-baqh">Rade</td>
<td class="tg-baqh">76.15</td>
<td class="tg-baqh">86.60</td>
<td class="tg-baqh">0.413</td>
<td class="tg-baqh">1.200</td>
<td class="tg-baqh">1.486</td>
</tr>
<tr>
<td class="tg-baqh">Rebuffi_R18</td>
<td class="tg-baqh">75.86</td>
<td class="tg-baqh">87.60</td>
<td class="tg-baqh">0.369</td>
<td class="tg-baqh">1.210</td>
<td class="tg-baqh">1.413</td>
</tr>
<tr>
<td class="tg-baqh">Gowal</td>
<td class="tg-baqh">74.50</td>
<td class="tg-baqh">86.40</td>
<td class="tg-baqh">0.124</td>
<td class="tg-baqh">1.116</td>
<td class="tg-baqh">1.253</td>
</tr>
<tr>
<td class="tg-baqh">Sehwag_R18</td>
<td class="tg-baqh">74.41</td>
<td class="tg-baqh">88.60</td>
<td class="tg-baqh">0.236</td>
<td class="tg-baqh">1.135</td>
<td class="tg-baqh">1.343</td>
</tr>
<tr>
<td class="tg-baqh">Wu2020Adversarial</td>
<td class="tg-baqh">73.66</td>
<td class="tg-baqh">84.60</td>
<td class="tg-baqh">0.128</td>
<td class="tg-baqh">1.110</td>
<td class="tg-baqh">1.369</td>
</tr>
<tr>
<td class="tg-baqh">Augustin2020Adversarial</td>
<td class="tg-baqh">72.91</td>
<td class="tg-baqh">85.20</td>
<td class="tg-baqh">0.569</td>
<td class="tg-baqh">1.199</td>
<td class="tg-baqh">1.285</td>
</tr>
<tr>
<td class="tg-baqh">Engstrom2019Robustness</td>
<td class="tg-baqh">69.24</td>
<td class="tg-baqh">82.20</td>
<td class="tg-baqh">0.160</td>
<td class="tg-baqh">1.020</td>
<td class="tg-baqh">1.084</td>
</tr>
<tr>
<td class="tg-baqh">Rice2020Overfitting</td>
<td class="tg-baqh">67.68</td>
<td class="tg-baqh">81.80</td>
<td class="tg-baqh">0.152</td>
<td class="tg-baqh">1.040</td>
<td class="tg-baqh">1.097</td>
</tr>
<tr>
<td class="tg-baqh">Rony2019Decoupling</td>
<td class="tg-baqh">66.44</td>
<td class="tg-baqh">79.20</td>
<td class="tg-baqh">0.275</td>
<td class="tg-baqh">1.101</td>
<td class="tg-baqh">1.165</td>
</tr>
<tr>
<td class="tg-baqh">Ding2020MMA</td>
<td class="tg-baqh">66.09</td>
<td class="tg-baqh">77.60</td>
<td class="tg-baqh">0.112</td>
<td class="tg-baqh">0.909</td>
<td class="tg-baqh">1.095</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</section>
<!-- Results -->
<!-- Model Ranking Comparison Section -->
<section class="section">
<div class="container is-max-desktop">
<h2 class="title is-3">Model Ranking Comparison</h2>
<div class="columns is-centered">
<div class="column is-full-width">
<div class="content has-text-justified">
<table class="table is-bordered is-striped is-narrow is-hoverable is-fullwidth">
<caption><strong>Table 2.</strong> Spearman's rank correlation coefficient on CIFAR-10 using GREAT Score, RobustBench (with test set), and Auto-Attack (with generated samples).</caption>
<thead>
<tr>
<th></th>
<th>Uncalibrated</th>
<th>Calibrated</th>
</tr>
</thead>
<tbody>
<tr>
<td>GREAT Score vs. RobustBench Correlation</td>
<td>0.6618</td>
<td>0.8971</td>
</tr>
<tr>
<td>GREAT Score vs. AutoAttack Correlation</td>
<td>0.3690</td>
<td>0.6941</td>
</tr>
<tr>
<td>RobustBench vs. AutoAttack Correlation</td>
<td>0.7296</td>
<td>0.7296</td>
</tr>
</tbody>
</table>
<p>
We compare the model ranking on CIFAR-10 using GREAT Score (evaluated with generated samples), RobustBench (evaluated with Auto-Attack on the test set), and Auto-Attack (evaluated with Auto-Attack on generated samples).
Table 2 presents their mutual rank correlation (higher value means more aligned ranking) with calibrated and uncalibrated versions.
We note that there is an innate discrepancy between Spearman's rank correlation coefficient (way below 1) of RobustBench vs. Auto-Attack, which means Auto-Attack will give inconsistent model rankings when evaluated on different data samples. In addition, GREAT Score measures <em>classification margin</em>, while AutoAttack measures <em>accuracy</em> under a fixed perturbation budget ε. AutoAttack's ranking will change if we use different ε values. E.g., comparing the ranking of ε=0.3 and ε=0.7 on 10000 CIFAR-10 test images for AutoAttack, the Spearman's correlation is only 0.9485. Therefore, we argue that GREAT Score and AutoAttack are <em>complementary</em> evaluation metrics and they don't need to match perfectly.
Despite their discrepancy, before calibration, the correlation between GREAT Score and RobustBench yields a similar value. With calibration, there is a significant improvement in rank correlation between GREAT Score to Robustbench and Auto-Attack, respectively.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- Model Ranking Comparison Section -->
<!-- GREAT Score vs CW Attack Comparison Section -->
<section class="section">
<div class="container is-max-desktop">
<h2 class="title is-3">GREAT Score vs CW Attack Comparison</h2>
<div class="columns is-centered">
<div class="column container-centered">
<div>
<img src="./static/images/new_figure_2_2.png"
class="method_overview"
alt="Comparison of local GREAT Score and CW attack"/>
<p>
<strong>Figure 2.</strong> Comparison of local GREAT Score and CW attack in L<sub>2</sub> perturbation on CIFAR-10 with Rebuffi_extra model.
The x-axis is the image id. The result shows the local GREAT Score is indeed a lower bound of the perturbation level found by CW attack.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- GREAT Score vs CW Attack Comparison Section -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{li2024greatscore,
title = {GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models},
author = {Zaitang, Li and Pin-Yu, Chen and Tsung-Yi, Ho},
journal = {NeurIPS},
year = {2024},
}</code></pre>
</div>
</section>
<footer class="footer">
<div class="container">
<!-- <div class="content has-text-centered">
<a class="icon-link" target="_blank"
href="./static/videos/nerfies_paper.pdf">
<i class="fas fa-file-pdf"></i>
</a>
<a class="icon-link" href="https://github.com/keunhong" target="_blank" class="external-link" disabled>
<i class="fab fa-github"></i>
</a>
</div> -->
<div class="columns is-centered">
<div class="column is-8">
<div class="content">
<p>
This website is licensed under a <a rel="license" target="_blank"
href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
Commons Attribution-ShareAlike 4.0 International License</a>.
</p>
<p>
This means you are free to borrow the <a target="_blank"
href="https://github.com/nerfies/nerfies.github.io">source code</a> of this website,
we just ask that you link back to this page in the footer.
Please remember to remove the analytics code included in the header of the website which
you do not want on your website.
</p>
</div>
</div>
</div>
</div>
</footer>
</body>
</html>
|