## Bar Chart: Performance Comparison (HIT@5) across Backdoor and Targeted Scenarios
### Overview
The image presents six grouped bar charts, organized in a 2x3 grid, evaluating the performance of four different methods ($BL_I$, $BL_{II}$, $ROAR_{kp}$, $ROAR_{co}$) across three different perturbation/poisoning levels (0%, 10%, 30%). The charts are categorized into two rows: "Backdoor" scenarios (top row) and "Targeted" scenarios (bottom row). The Y-axis measures "HIT@5," a metric representing the probability of the correct item appearing in the top 5 predictions.
### Components/Axes
* **Y-Axis:** Labeled "HIT@5," ranging from 0.00 to 1.00.
* **X-Axis:** Categorical levels of perturbation/poisoning: 0%, 10%, and 30%.
* **Legend:** Located at the top-center and top-right of the respective rows.
* **$BL_I$**: Light Green
* **$BL_{II}$**: Dark Green/Teal
* **$ROAR_{kp}$**: Light Blue
* **$ROAR_{co}$**: Dark Blue
* **Sub-chart Titles:**
* (a) Backdoor-Vulnerability
* (b) Backdoor-Diagnosis
* (c) Backdoor-Freebase
* (d) Targeted-Vulnerability
* (e) Targeted-Diagnosis
* (f) Targeted-Freebase
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### Detailed Analysis
#### Row 1: Backdoor Scenarios
*General Trend:* In all three Backdoor charts, the $ROAR$ methods ($ROAR_{kp}$ and $ROAR_{co}$) significantly outperform the $BL$ methods ($BL_I$ and $BL_{II}$). Performance for all methods generally trends downward as the percentage increases from 0% to 30%.
** (a) Backdoor-Vulnerability**
* **0%:** $BL_I$: 0.12, $BL_{II}$: 0.05, $ROAR_{kp}$: 0.55, $ROAR_{co}$: 0.71
* **10%:** $BL_I$: 0.04, $BL_{II}$: 0.00, $ROAR_{kp}$: 0.45, $ROAR_{co}$: 0.57
* **30%:** $BL_I$: 0.00, $BL_{II}$: 0.00, $ROAR_{kp}$: 0.32, $ROAR_{co}$: 0.43
** (b) Backdoor-Diagnosis**
* **0%:** $BL_I$: 0.22, $BL_{II}$: 0.02, $ROAR_{kp}$: 0.37, $ROAR_{co}$: 0.52
* **10%:** $BL_I$: 0.13, $BL_{II}$: 0.00, $ROAR_{kp}$: 0.30, $ROAR_{co}$: 0.44
* **30%:** $BL_I$: 0.08, $BL_{II}$: 0.00, $ROAR_{kp}$: 0.20, $ROAR_{co}$: 0.39
** (c) Backdoor-Freebase**
* **0%:** $BL_I$: 0.12, $BL_{II}$: 0.09, $ROAR_{kp}$: 0.62, $ROAR_{co}$: 0.88
* **10%:** $BL_I$: 0.10, $BL_{II}$: 0.03, $ROAR_{kp}$: 0.56, $ROAR_{co}$: 0.70
* **30%:** $BL_I$: 0.04, $BL_{II}$: 0.00, $ROAR_{kp}$: 0.44, $ROAR_{co}$: 0.57
#### Row 2: Targeted Scenarios
*General Trend:* In the Targeted scenarios, the $BL$ methods ($BL_I$ and $BL_{II}$) significantly outperform the $ROAR$ methods. Performance is generally more stable across the 0%-30% range compared to the Backdoor scenarios.
** (d) Targeted-Vulnerability**
* **0%:** $BL_I$: 0.88, $BL_{II}$: 0.93, $ROAR_{kp}$: 0.72, $ROAR_{co}$: 0.06
* **10%:** $BL_I$: 0.90, $BL_{II}$: 0.95, $ROAR_{kp}$: 0.80, $ROAR_{co}$: 0.13
* **30%:** $BL_I$: 0.71, $BL_{II}$: 0.74, $ROAR_{kp}$: 0.68, $ROAR_{co}$: 0.39
** (e) Targeted-Diagnosis**
* **0%:** $BL_I$: 0.62, $BL_{II}$: 0.65, $ROAR_{kp}$: 0.44, $ROAR_{co}$: 0.01
* **10%:** $BL_I$: 0.68, $BL_{II}$: 0.76, $ROAR_{kp}$: 0.54, $ROAR_{co}$: 0.10
* **30%:** $BL_I$: 0.68, $BL_{II}$: 0.66, $ROAR_{kp}$: 0.55, $ROAR_{co}$: 0.20
** (f) Targeted-Freebase**
* **0%:** $BL_I$: 0.56, $BL_{II}$: 0.61, $ROAR_{kp}$: 0.33, $ROAR_{co}$: 0.23
* **10%:** $BL_I$: 0.60, $BL_{II}$: 0.62, $ROAR_{kp}$: 0.41, $ROAR_{co}$: 0.37
* **30%:** $BL_I$: 0.53, $BL_{II}$: 0.54, $ROAR_{kp}$: 0.52, $ROAR_{co}$: 0.40
---
### Key Observations
* **Method Specialization:** There is a clear dichotomy in performance. $ROAR$ methods are highly effective in Backdoor scenarios but struggle in Targeted scenarios. Conversely, $BL$ methods are highly effective in Targeted scenarios but perform poorly (often near zero) in Backdoor scenarios.
* **Performance Degradation:** In Backdoor scenarios, increasing the perturbation/poisoning percentage consistently degrades performance across all methods.
* **Notable Outliers:**
* $ROAR_{co}$ in Targeted-Diagnosis (0%) shows an extremely low HIT@5 of 0.01.
* $BL_{II}$ in Backdoor-Diagnosis (10% and 30%) and Backdoor-Vulnerability (30%) hits 0.00, indicating a complete failure to identify the target in those conditions.
### Interpretation
The data demonstrates that the choice of method is highly dependent on the specific threat model (Backdoor vs. Targeted). The $ROAR$ family of methods appears to be specifically engineered or suited for detecting or mitigating Backdoor attacks, as evidenced by their dominance in the top row. The $BL$ family appears to be a baseline or alternative approach that is better suited for Targeted scenarios, where it maintains high accuracy while $ROAR$ methods falter. The inverse relationship between the two method families suggests they may be optimizing for different features or utilizing different underlying logic that is mutually exclusive in terms of high performance.
