Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation

1 Introduction . Knowledge distillation ( KD ) [ 10 ] aims to transfer the useful knowledge of a trained model ( the teacher ) to another model ( the student ) . KD has found success in various applications [ 1 , 29 , 3 , 17 ] and is particularly useful for resource-constrained IoT applications where the compute budget is limited and compute-efficient models are required . Generally , KD requires the student model to be trained over the same data-set that is used to train the teacher . However , recent research [ 31 , 20 , 2 ] has shown the efficacy of KD even under the `` data-free '' scenario where the training data may not be available for the student to get trained . Over the past few years various forms of distillation have been proposed , including distillation from the student itself [ 36 ] and via an ensemble of students [ 37 ] . However , recently , reference [ 18 ] has highlighted the fact that many forms of distillation may unintentionally leak the IP of a teacher model . In particular , some teacher models contain significant IP associated with the arduous effort of both data collection and model training , which motivates their release as “ black-box '' executable pieces of software . Moreover , these trained models may even enable safeguarding the training data ( for example , sensitive medical images [ 28 ] and company proprietary information [ 27 , 30 ] ) as well as their performance on the secure data . Use of KD under these circumstances , sometimes 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . under the “ data-free '' condition , may enable training of an unauthorized student to yield comparable performance as the teacher . To mitigate this issue , reference [ 18 ] has proposed the idea of a nasty teacher that prevents knowledge leaking to a student and thereby reduces the student ’ s classification performance . Fig . 1 ( a ) depicts the the success of a nasty ResNet50 in degrading various student models ’ classification performances compared to their respective baseline performances1 . Our contributions : Earlier evaluations [ 18 ] have shown that nasty teachers can retain their efficacy under various settings of two key hyperparameters , namely the weight of the distillation loss ( α ) and the softmax temperature ( τ ) of the distillation loss . However , a comprehensive evaluation of the efficacy of undistillable nasty teachers has yet to be completed . Towards this goal , we investigate the performance of KD on distillation at different depths of the student model , and in particular , how the teacher ’ s influence changes when transferring knowledge to an intermediate shallow section of the student through an auxiliary classifier ( AC ) . We find that the impact of the nasty teacher drastically reduces as we transfer knowledge to a shallow subsection of the student ( Fig . 1 ( c ) ) . Based on these findings , we present a skeptical student that uses an intermediate shallow auxiliary classifier to transfer the information derived through the soft probabilities of the teacher ’ s output classes . We further propose a novel hybrid distillation scheme to improve learnability of the student by distilling both from a teacher and the student itself . Our approach has some similarity with selfdistillation ( SD ) [ 36 ] because both approaches use an auxiliary classifier for the knowledge transfer . However , the goal of SD is to show the efficacy of a model distilling from itself , contrasting our goal of analyzing the possible presence of a potential model stealer who can extract knowledge even from an undistillable nasty teacher . More importantly , the proposed hybrid distillation is effective in stealing a model ’ s IP even under a “ data-free '' scenario , contrasting SD which is only applicable for students who have access to training data . We conduct extensive experiments using both standard KD with available training data on CIFAR-10 , CIFAR-100 , and Tiny-ImageNet , and data-free KD on CIFAR-10 testset . Experimental results show that compared to normal ones , skeptical students exhibit improved performance of up to ∼59.5 % and ∼5.8 % for data-available and data-free KD , respectively , when distilled from nasty teachers . This exposes a significant limitation of nasty teachers attempting to protect model IP . Moreover , our proposed students perform similar to normal student models while distilled from normal teachers , demonstrating their efficacy irrespective of the teacher being nasty or not . 2 Preliminaries . 2.1 Knowledge Distillation . Knowledge distillation , similar to the goal of various model compression techniques [ 13 , 7 , 5 , 14 , 16 ] , embeds the rich information of a compute-heavy model into a model that generally requires fewer computations . The traditional KD [ 10 ] relies on information transfer through a Kullback–Leibler ( KL ) divergence measure between the soft logits at the output classifier layers of the teacher and the student . Apart from this , over the past few years , various other efficient distillation methods have been proposed , including distilling from hints provided by the teacher [ 25 ] , distilling via attention transfer [ 34 ] , and other approaches [ 23 , 21 ] . To reduce the distillation-based training time and avoid the requirement of a separate teacher , reference [ 36 ] has proposed self-distillation . The authors partition 1The baseline models are trained with only cross-entropy ( CE ) loss . the student model into several shallow sections , each having its own auxiliary classifier , to which the final classifier transfers the soft-logits to enhance the model ’ s classification performance . Several recent studies [ 32 , 33 ] have also shown the efficacy of KD to a student from its own pre-trained variant as its teacher . Similar to [ 18 ] , in this work , we assume to have no access to the teacher model ’ s intermediate features . Rather , we focus on the information leaking to an unauthorized outsider and thus rely on standard KD . 2.2 Model IP protection . Protection of model IP has drawn significant interest primarily due to the massive human resource and financial costs required for large model training . Earlier works explored various defense strategies , including adaptive misinformation against model performance stealing [ 11 ] and passport-based defense [ 35 ] . In most of these cases , the stealer is assumed to have access to only synthetically generated data . However , also of interest is when a portion of training data is unintentionally leaked . 2.3 Poisoning of Neural Network Models . An attacker can degrade the performance of a neural network by simply injecting poisoned data [ 22 ] into the training set . Adversarial-attack generated images [ 6 , 19 , 15 ] have proven to be effective in degrading a model ’ s performance . Backdoor attacks [ 4 ] insert crafted malicious data into the training set that apparently trains the model to perform well until such time that the attacker sets up a signal that degrades the model performance drastically . The Bit flip attack [ 24 ] corrupts selective bits of the trained DNN weights to lower its performance . As mentioned earlier , reference [ 18 ] has recently proposed to poison a neural network model through training , such that the model retains its classification performance , but loses its ability to be used as an efficient teacher , thus referred to as a nasty teacher . These nasty teachers are believed to protect the model IP.2 This work analyzes the degree of IP protection that such teachers provide and , in particular , presents a skeptical student-based distillation technique that diminishes the effect of their nastiness , as detailed in the next Section . 3 Motivational Case Study . To motivate our skeptical students , this section presents an empirical analysis that explore the efficacy of nasty teacher models under two distinct KD scenarios3 . 3.1 Transferability of the Impact of Nasty Teachers . Definition 1 . Secondary student : We define a secondary student as a model that is trained via KD from a trained model which was earlier trained via distillation from a teacher model . In this context , we refer to the student that is distilled from the original teacher model , as the primary student . We measure the transferability impact ( TI ) of a teacher as the performance improvement ( or degradation ) of a secondary student with respect to its baseline ( ∆base ) . A negative ∆base signifies the success of a teacher ’ s privacy or confidentiality preserving effort [ 18 ] . 2In so far as a teacher is trying to protect its private IP from inquiring or even intrusive students , we believe a better phrase to characterize him/her is as a “ defensive teacher '' or in the worst case as a “ secretive teacher '' . In our view , there is nothing “ nasty '' about what the teacher is attempting to do . However , because the prior art has consistently used the term “ nasty teacher , '' we will also use that phrase in this paper . 3The description of training hyperparameters is given in Section 5.1 for all the experiments in this section . We first trained both normal as well as nasty variants of a ResNet50 on CIFAR-100 . We then used two ResNet18 students to distill knowledge from these two teachers . Finally , to test TI we used the distilled ResNet18 models as teachers to secondary students with identical model architectures . Observation 1 . The TI of a nasty teacher is negative , meaning the nastiness of a teacher transfers to its student . Table 1 shows the classification performance of a secondary student is 6.56 % lower than the baseline while using a distilled ResNet18 as teacher . Interestingly , this implies that the false sense of generalization that the primary student inherits makes it become a nasty teacher . Thus simple redistillation from a primary to a secondary student will not evade the nastiness of a teacher . 4 3.2 Transferring Knowledge to a Shallow Subsection of the Student . We computed the weighted KL-divergence based distillation loss at an auxiliary classifier ( AC ) of the student , and used its final classifier ( C ) to compute the weighted CE-loss only . We evaluated the impact of the auxiliary classifier placed at different depths of the student model . In particular , for the ResNet18 student we performed four experiments by placing the AC after the nth BB , n ranging from 1 to 4 , where the model ’ s final classifier is located at the end of 4th block . We follow similar procedure as [ 36 ] to design the AC branches . Observation 2 . The influence of a teacher ( both nasty and normal ) on a student ’ s performance reduces as we distill knowledge to a shallow subsection of the student model . Fig . 2 shows the accuracy of the student model approaches the baseline accuracy as we distill from the teacher at shallower depths ( n = 1 ) . Interestingly , this trend can be observed for both nasty and normal teachers . In particular , from a nasty ResNet50 , the student ResNet18 has a test accuracy of up to 77.19 % while distilled at AC 1 , in contrast the accuracy is 72.47 % when distilled at the final classifier . On the other hand , ResNet18 can have a classification accuracy reduction of as much as 1.56 % when distilled at shallow depths . We leverage these two observations in our hybrid distillation on the skeptical students and diminish the transferring of teachers ’ nastiness , presented in the next section .

Summary: The authors investigate the knowledge distillation immunity of Nasty teachers. A nasty teacher is a model that maintains good prediction performances but significantly degrades the performances of student models that distill knowledge from it. In this paper, the authors proposed a hybrid distillation that neutralizes the protection of nasty teachers, allowing the training of accurate student models.