Image Classification Defense on Tiny-ImageNet (CA, ASR)
48.52Clean Accuracy (CA)LAC
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| LACQuantization=8-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 48.52 | 1.49 | |
| QVec (0.98)Quantization=8-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 48.32 | 1.53 | |
| Gaussian NoiseQuantization=8-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 48.12 | 6.53 | |
| QVec (0.98)Quantization=8-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 47.55 | 0.23 | |
| QVec (0.95)Quantization=8-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 47.48 | 0.27 | |
| EFRAPQuantization=8-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 47.16 | 1.83 | |
| LACQuantization=8-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 47.11 | 3.59 | |
| EFRAPQuantization=8-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 47.06 | 4.21 | |
| No defenseQuantization=8-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 46.84 | 97.48 | |
| Gaussian NoiseQuantization=8-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 46.81 | 54.73 | |
| No defenseQuantization=8-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 46.69 | 97.95 | |
| QVec (0.95)Quantization=8-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 45.96 | 0.74 | |
| QVec (0.98)Quantization=4-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 36.63 | 1.38 | |
| QVec (0.95)Quantization=4-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 35.98 | 1.49 | |
| No defenseQuantization=4-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 32.58 | 98.15 | |
| QVec (0.98)Quantization=4-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 32.43 | 1.69 | |
| LACQuantization=4-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 31.5 | 1.32 | |
| QVec (0.95)Quantization=4-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 30.98 | 12.86 | |
| EFRAPQuantization=4-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 29.62 | 2.39 | |
| Gaussian NoiseQuantization=4-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 27.49 | 25.06 | |
| LACQuantization=4-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 20.4 | 36.74 | |
| EFRAPQuantization=4-bit, Attack Scenario=Exploiting LLM, Model=MobileNetV22026.06 | 19.52 | 38.62 | |
| No defenseQuantization=4-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 18.6 | 90.24 | |
| Gaussian NoiseQuantization=4-bit, Attack Scenario=Qu-Anti-zation, Model=MobileNetV22026.06 | 3.11 | 31.31 |