Finding Deviated Behaviors of the Compressed DNN Models for Image Classifications
Bibliographic record
Abstract
Model compression can significantly reduce the sizes of deep neural network (DNN) models and thus facilitate the dissemination of sophisticated, sizable DNN models, especially for deployment on mobile or embedded devices. However, the prediction results of compressed models may deviate from those of their original models. To help developers thoroughly understand the impact of model compression, it is essential to test these models to find thosedeviated behaviorsbefore dissemination. However, this is a non-trivial task, because the architectures and gradients of compressed models are usually not available. To this end, we proposeDflare, a novel, search-based, black-box testing technique to automatically find triggering inputs that result in deviated behaviors in image classification tasks.Dflareiteratively applies a series of mutation operations to a given seed image until a triggering input is found. For better efficacy and efficiency,Dflaremodels the search problem as Markov Chains and leverages the Metropolis-Hasting algorithm to guide the selection of mutation operators in each iteration. Further,Dflareutilizes a novel fitness function to prioritize the mutated inputs that either cause large differences between two models’ outputs or trigger previously unobserved models’ probability vectors. We evaluatedDflareon 21 compressed models for image classification tasks with three datasets. The results show thatDflarenot only constantly outperforms the baseline in terms of efficacy but also significantly improves the efficiency:Dflareis 17.84×–446.06× as fast as the baseline in terms of time; the number of queries required byDflareto find one triggering input is only 0.186–1.937% of those issued by the baseline. We also demonstrated that the triggering inputs found byDflarecan be used to repair up to 48.48% deviated behaviors in image classification tasks and further decrease the effectiveness ofDflareon the repaired models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".