Uneven Licensed Robustness through Characteristic-Convex Neural Networks – The Berkeley Synthetic Intelligence Analysis Weblog

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Uneven Licensed Robustness through Characteristic-Convex Neural Networks

TLDR: We suggest the uneven licensed robustness downside, which requires licensed robustness for just one class and displays real-world adversarial situations. This targeted setting permits us to introduce feature-convex classifiers, which produce closed-form and deterministic licensed radii on the order of milliseconds.

diagram illustrating the FCNN architecture

Determine 1. Illustration of feature-convex classifiers and their certification for sensitive-class inputs. This structure composes a Lipschitz-continuous function map $varphi$ with a realized convex operate $g$. Since $g$ is convex, it’s globally underapproximated by its tangent aircraft at $varphi(x)$, yielding licensed norm balls within the function house. Lipschitzness of $varphi$ then yields appropriately scaled certificates within the unique enter house.

Regardless of their widespread utilization, deep studying classifiers are acutely weak to adversarial examples: small, human-imperceptible picture perturbations that idiot machine studying fashions into misclassifying the modified enter. This weak spot severely undermines the reliability of safety-critical processes that incorporate machine studying. Many empirical defenses in opposition to adversarial perturbations have been proposed—usually solely to be later defeated by stronger assault methods. We subsequently give attention to certifiably strong classifiers, which offer a mathematical assure that their prediction will stay fixed for an $ell_p$-norm ball round an enter.

Standard licensed robustness strategies incur a variety of drawbacks, together with nondeterminism, gradual execution, poor scaling, and certification in opposition to just one assault norm. We argue that these points could be addressed by refining the licensed robustness downside to be extra aligned with sensible adversarial settings.

The Uneven Licensed Robustness Drawback

Present certifiably strong classifiers produce certificates for inputs belonging to any class. For a lot of real-world adversarial purposes, that is unnecessarily broad. Take into account the illustrative case of somebody composing a phishing rip-off electronic mail whereas making an attempt to keep away from spam filters. This adversary will all the time try and idiot the spam filter into considering that their spam electronic mail is benign—by no means conversely. In different phrases, the attacker is solely making an attempt to induce false negatives from the classifier. Comparable settings embody malware detection, faux information flagging, social media bot detection, medical insurance coverage claims filtering, monetary fraud detection, phishing web site detection, and plenty of extra.

a motivating spam-filter diagram

Determine 2. Uneven robustness in electronic mail filtering. Sensible adversarial settings usually require licensed robustness for just one class.

These purposes all contain a binary classification setting with one delicate class that an adversary is making an attempt to keep away from (e.g., the “spam electronic mail” class). This motivates the issue of uneven licensed robustness, which goals to offer certifiably strong predictions for inputs within the delicate class whereas sustaining a excessive clear accuracy for all different inputs. We offer a extra formal downside assertion in the primary textual content.

Characteristic-convex classifiers

We suggest feature-convex neural networks to deal with the uneven robustness downside. This structure composes a easy Lipschitz-continuous function map ${varphi: mathbb{R}^d to mathbb{R}^q}$ with a realized Enter-Convex Neural Community (ICNN) ${g: mathbb{R}^q to mathbb{R}}$ (Determine 1). ICNNs implement convexity from the enter to the output logit by composing ReLU nonlinearities with nonnegative weight matrices. Since a binary ICNN determination area consists of a convex set and its complement, we add the precomposed function map $varphi$ to allow nonconvex determination areas.

Characteristic-convex classifiers allow the quick computation of sensitive-class licensed radii for all $ell_p$-norms. Utilizing the truth that convex capabilities are globally underapproximated by any tangent aircraft, we are able to receive an authorized radius within the intermediate function house. This radius is then propagated to the enter house by Lipschitzness. The uneven setting right here is vital, as this structure solely produces certificates for the positive-logit class $g(varphi(x)) > 0$.

The ensuing $ell_p$-norm licensed radius components is especially elegant:

[r*p(x) = frac{ color{blue}{g(varphi(x))} } { mathrm{Lip}_p(varphi) color{red}{| nabla g(varphi(x)) | *{p,*}}}.]

The non-constant phrases are simply interpretable: the radius scales proportionally to the classifier confidence and inversely to the classifier sensitivity. We consider these certificates throughout a variety of datasets, attaining aggressive $ell_1$ certificates and comparable $ell_2$ and $ell_{infty}$ certificates—regardless of different strategies typically tailoring for a selected norm and requiring orders of magnitude extra runtime.

cifar10 cats dogs certified radii

Determine 3. Delicate class licensed radii on the CIFAR-10 cats vs canines dataset for the $ell_1$-norm. Runtimes on the proper are averaged over $ell_1$, $ell_2$, and $ell_{infty}$-radii (notice the log scaling).

Our certificates maintain for any $ell_p$-norm and are closed type and deterministic, requiring only one forwards and backwards go per enter. These are computable on the order of milliseconds and scale nicely with community dimension. For comparability, present state-of-the-art strategies corresponding to randomized smoothing and interval certain propagation usually take a number of seconds to certify even small networks. Randomized smoothing strategies are additionally inherently nondeterministic, with certificates that simply maintain with excessive likelihood.

Theoretical promise

Whereas preliminary outcomes are promising, our theoretical work suggests that there’s important untapped potential in ICNNs, even and not using a function map. Regardless of binary ICNNs being restricted to studying convex determination areas, we show that there exists an ICNN that achieves good coaching accuracy on the CIFAR-10 cats-vs-dogs dataset.

Reality. There exists an input-convex classifier which achieves good coaching accuracy for the CIFAR-10 cats-versus-dogs dataset.

Nonetheless, our structure achieves simply $73.4%$ coaching accuracy and not using a function map. Whereas coaching efficiency doesn’t indicate take a look at set generalization, this end result means that ICNNs are no less than theoretically able to attaining the trendy machine studying paradigm of overfitting to the coaching dataset. We thus pose the next open downside for the sphere.

Open downside. Be taught an input-convex classifier which achieves good coaching accuracy for the CIFAR-10 cats-versus-dogs dataset.


We hope that the uneven robustness framework will encourage novel architectures that are certifiable on this extra targeted setting. Our feature-convex classifier is one such structure and supplies quick, deterministic licensed radii for any $ell_p$-norm. We additionally pose the open downside of overfitting the CIFAR-10 cats vs canines coaching dataset with an ICNN, which we present is theoretically attainable. Please see our NeurIPS paper and codebase for extra particulars.

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