Shiqing Ma (Purdue University), Yingqi Liu (Purdue University), Guanhong Tao (Purdue University), Wen-Chuan Lee (Purdue University), Xiangyu Zhang (Purdue University)

Deep Neural Networks (DNN) are vulnerable to adversarial samples that
are generated by perturbing correctly classified inputs to cause DNN
models to misbehave (e.g., misclassification). This can potentially
lead to disastrous consequences especially in security-sensitive
applications. Existing defense and detection techniques work well for
specific attacks under various assumptions (e.g., the set of possible
attacks are known beforehand). However, they are not sufficiently
general to protect against a broader range of attacks. In this paper,
we analyze the internals of DNN models under various attacks and
identify two common exploitation channels: the provenance channel and
the activation value distribution channel. We then propose a novel
technique to extract DNN invariants and use them to perform runtime
adversarial sample detection. Our experimental results of 11 different
kinds of attacks on popular datasets including ImageNet and 13 models
show that our technique can effectively detect all these attacks
(over 90% accuracy) with limited false positives. We also compare it
with three state-of-the-art techniques including the Local Intrinsic
Dimensionality (LID) based method, denoiser based methods (i.e.,
MagNet and HGD), and the prediction inconsistency based approach
(i.e., feature squeezing). Our experiments show promising results.

View More Papers

Digital Healthcare-Associated Infection: A Case Study on the Security...

Luis Vargas (University of Florida), Logan Blue (University of Florida), Vanessa Frost (University of Florida), Christopher Patton (University of Florida), Nolen Scaife (University of Florida), Kevin R.B. Butler (University of Florida), Patrick Traynor (University of Florida)

Read More

Measuring the Facebook Advertising Ecosystem

Athanasios Andreou (EURECOM), Márcio Silva (UFMG), Fabrício Benevenuto (UFMG), Oana Goga (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG), Patrick Loiseau (Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG & MPI-SWS), Alan Mislove (Northeastern University)

Read More

Statistical Privacy for Streaming Traffic

Xiaokuan Zhang (The Ohio State University), Jihun Hamm (The Ohio State University), Michael K. Reiter (University of North Carolina at Chapel Hill), Yinqian Zhang (The Ohio State University)

Read More

Distinguishing Attacks from Legitimate Authentication Traffic at Scale

Cormac Herley (Microsoft), Stuart Schechter (Unaffiliated)

Read More