Adversarial classification is the task of performing robust classification in the presence of a strategic attacker. Originating from information hiding and multimedia forensics, adversarial classification recently received a lot of attention in a broader security context. In the domain of machine learning-based image classification, adversarial classification can be interpreted as detecting so-called adversarial examples, which are slightly altered versions of benign images. They are specifically crafted to be misclassified with a very high probability by the classifier under attack. Neural networks, which dominate among modern image classifiers, have been shown to be especially vulnerable to these adversarial examples. However, detecting subtle changes in digital images has always been the goal of multimedia forensics and steganalysis, two major subfields of multimedia security. We highlight the conceptual similarities between these fields and secure machine learning. Furthermore, we adapt a linear filter, similar to early steganalysis methods, to detect adversarial examples that are generated with the projected gradient descent (PGD) method, the state-of-the-art algorithm for this task. We test our method on the MNIST database and show for several parameter combinations of PGD that our method can reliably detect adversarial examples. Additionally, the combination of adversarial re-training and our detection method effectively reduces the attack surface of attacks against neural networks. Thus, we conclude that adversarial examples for image classification possibly do not withstand detection methods from steganalysis, and future work should explore the effectiveness of known techniques from multimedia security in other adversarial settings.
Detecting adversarial examples - A lesson from multimedia security / Schottle, P.; Schlogl, A.; Pasquini, C.; Bohme, R.. - 2018-(2018), pp. 947-951. ((Intervento presentato al convegno 26th European Signal Processing Conference, EUSIPCO 2018 tenutosi a Roma, Italia nel 2018.
Scheda prodotto non validato
I dati visualizzati non sono stati ancora sottoposti a validazione formale da parte dello Staff di IRIS, ma sono stati ugualmente trasmessi al Sito Docente Cineca (Loginmiur).
|Titolo:||Detecting adversarial examples - A lesson from multimedia security|
|Autori:||Schottle, P.; Schlogl, A.; Pasquini, C.; Bohme, R.|
|Titolo del volume contenente il saggio:||European Signal Processing Conference|
|Luogo di edizione:||Rome, Italia|
|Casa editrice:||European Signal Processing Conference, EUSIPCO|
|Anno di pubblicazione:||2018|
|Codice identificativo Scopus:||2-s2.0-85059814924|
|Codice identificativo ISI:||WOS:000455614900191|
|Citazione:||Detecting adversarial examples - A lesson from multimedia security / Schottle, P.; Schlogl, A.; Pasquini, C.; Bohme, R.. - 2018-(2018), pp. 947-951. ((Intervento presentato al convegno 26th European Signal Processing Conference, EUSIPCO 2018 tenutosi a Roma, Italia nel 2018.|
|Appare nelle tipologie:|