Paper: Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects (ICLR-2024)

Reviewer comment (Experiments):
Though the generalizability of Camouflageator has been validated with ResNet50 based COD methods, the paper does not conduct experiments with other COD methods employing Res2Net50 and Swin as backbones.

Author response:
**Q3: Verify the generalizability of Camouflageator on the COD methods with Res2Net50 and Swin backbones.**

We further assess the generalizability of Camouflageator on detectors with various backbones, including Res2Net50-based, Swin Transformer-based, and PVT V2-based detectors. We consider two conditions: (1) the backbone of the auxiliary generator adopts ResNet50; (2) the backbone of the auxiliary generator aligns with the detector. If the original method is denoted as ICEG, we term the methods optimized in condition 1 and condition 2 as ICEG+ and ICEG++, respectively. Results presented in the table reveal that Camouflageator consistently enhances the performance of these detectors in both conditions, with greater improvement observed when the generator shares the same backbone with the detector. Specifically, Camouflageator improves ICEG by 2.9%, 3.2%, and 4.3% when the backbone of the generator and the detector are Res2Net50, Swin Transformer, and PVT V2. Furthermore, Camouflageator enhances BGNet, a state-of-the-art Res2Net50-based detector, by 2.3% (BGNet+ with ResNet50-based generator) and 2.7% (BGNet++ with Res2Net50-based generator). For the Swin Transformer-based detector, Camouflageator improves ICON by 1.5% (ICON+ with ResNet50-based generator) and 3.7% (ICON++ with Swin Transformer-based generator). Additionally, HitNet, a PVT V2-based detector, exhibits improvements with our Camouflageator, showing an increase of 0.9% (HitNet+ with ResNet50-based generator) and 3.9% (HitNet++ with PVT V2-based generator). 

|Methods||Backbones|*CHAMELEON*||||*CAMO*||||*COD10K*||||*NC4K*||||
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
||||$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|
|BGNet||Res2Net50|0.029|0.835|0.944|0.895|0.073|0.744|0.870|0.812|0.033|0.714|0.901|0.831|0.044|0.786|0.907|0.851|
|BGNet+||Res2Net50|0.027|0.845|0.952|0.898|0.070|0.760|0.882|0.818|0.031|0.729|0.913|0.838|0.042|0.800|0.916|0.860|
|BGNet++ (Res2Net50-based generator)||Res2Net50|0.027|0.847|0.956|0.903|0.070|0.761|0.885|0.820|0.030|0.735|0.918|0.833|0.042|0.805|0.922|0.863|
|ICEG||Res2Net50|0.025|0.869|0.958|0.908|0.066|0.808|0.903|0.838|0.028|0.752|0.914|0.845|0.042|0.828|0.917|0.867|
|ICEG+||Res2Net50|0.023|0.873|0.960|0.910|0.064|0.826|0.912|0.845|0.026|0.770|0.925|0.853|0.040|0.844|0.928|0.878|
|ICEG++ (Res2Net50-based generator)||Res2Net50|0.023|0.876|0.966|0.915|0.063|0.833|0.916|0.849|0.026|0.777|0.930|0.855|0.039|0.847|0.932|0.879|
|ICON||Swin|0.029|0.848|0.940|0.898|0.058|0.794|0.907|0.840|0.033|0.720|0.888|0.818|0.041|0.817|0.916|0.858|
|ICON+||Swin|0.028|0.855|0.943|0.899|0.056|0.808|0.915|0.843|0.031|0.728|0.895|0.822|0.040|0.823|0.922|0.861|
|ICON++ (Swin transformer-based generator)||Swin|0.026|0.862|0.949|0.902|0.055|0.823|0.926|0.849|0.029|0.749|0.906|0.830|0.037|0.836|0.931|0.866|
|ICEG||Swin|0.023|0.860|0.959|0.905|0.044|0.855|0.926|0.867|0.024|0.782|0.930|0.857|0.034|0.855|0.932|0.879|
|ICEG+||Swin|0.022|0.867|0.961|0.908|0.042|0.861|0.931|0.871|0.023|0.788|0.934|0.862|0.033|0.861|0.937|0.883|
|ICEG++ (Swin transformer-based generator)||Swin|0.021|0.883|0.969|0.911|0.041|0.868|0.938|0.876|0.021|0.803|0.945|0.865|0.032|0.869|0.944|0.885|
|HitNet||PVT|0.024|0.861|0.944|0.907|0.060|0.791|0.892|0.834|0.027|0.790|0.922|0.847|0.042|0.825|0.911|0.858|
|HitNet+||PVT|0.023|0.863|0.945|0.909|0.060|0.797|0.895|0.835|0.026|0.794|0.925|0.849|0.041|0.829|0.916|0.860|
|HitNet++ (PVT-based generator)||PVT|0.022|0.879|0.957|0.916|0.057|0.820|0.913|0.839|0.023|0.818|0.936|0.853|0.037|0.849|0.931|0.867|
|ICEG||PVT|0.022|0.879|0.957|0.913|0.043|0.863|0.933|0.876|0.022|0.805|0.938|0.871|0.030|0.869|0.941|0.890|
|ICEG+||PVT|0.022|0.881|0.958|0.915|0.041|0.867|0.935|0.877|0.021|0.811|0.939|0.873|0.029|0.875|0.944|0.893|
|ICEG++ (PVT-based generator)||PVT|0.021|0.889|0.965|0.920|0.039|0.880|0.949|0.881|0.019|0.832|0.968|0.903|0.025|0.896|0.960|0.908|

Reviewer comment (Experiments):
Please provide related statistics about the synthesized camouflaged objects? For example, the number of categories and the camouflaged property (e.g., color, contrasts, intensity comparisons of foreground and background).

Author response:
Thanks for the valuable comments. 

**Q1: Descriptions of the quantity or quality of the synthesized camouflaged objects.**

**Quantitative analysis**: There are three critical aspects to consider in maintaining the undetectability of a camouflaged object: (1) internal consistency (IC); (2) edge disruption (ED); (3) environment adaptation (EA). To assess the camouflaged level, accounting for color and contrast, we have designed three corresponding metrics: 

$IC = \|\mathbf{y} \otimes \mathbf{x}_g-P_o^{I}\|^2,$

$ED = \| \mathbf{y}_e \otimes \mathbf{x}_g - P_e^{I} \|^2,$

$EA = \|\mathbf{x}_g-P_g^{I}\|^2,$

where $\mathbf{x}_g$ is the synthesized camouflaged image, $\mathbf{y}$ is the ground truth binary mask, $\otimes$ denotes element-wise multiplication, $\mathbf{y}_e$ is the weighted edge mask dilated by Gaussian function. $P_o^{I}$ is the image-level object prototype, averaging foreground pixels. $P_e^{I}$ is the image-level edge prototype, averaging edge pixels specified by $\mathbf{y}_e$. $P_g^{I}$ is the image-level object prototype, averaging global pixels.

For clarity and comparability, we normalize these three metrics between [0,1] and **smaller values** indicate more covert camouflaged objects. It's essential to note that the generation of additional camouflaged objects occurs exclusively on the training set. Consequently, we evaluate the enhanced camouflaged images and the original camouflaged images solely on the training set. As shown in the table, the enhanced camouflaged images generated by our Camouflageator are more deceptive than the original images.

|Datasets|CAMO|(1000 |images)|COD10K|(3040 |images)|
|-|-|-|-|-|-|-|
||IC|ED|EA|IC|ED|EA|
|Original|0.415|0.627|0.339|0.508|0.609|0.348|
|Enhanced|0.086|0.092|0.103|0.096|0.102|0.091|

**Qualitative analysis**: As shown in Fig. 6 in the manuscript, our Camouflageator can generate more concealed objects by hiding discriminative information. For example, the mouth and eyes of the fish are deliberately hidden in our synthesized image, and the physiological information of the rabbit is also weakened.

Reviewer comment (Experiments):
Please supplement the experiments regarding the Camouflageator generalizability with Res2Net50 and Swin based methods?

Author response:
**Q3: Verify the generalizability of Camouflageator on the COD methods with Res2Net50 and Swin backbones.**

We further assess the generalizability of Camouflageator on detectors with various backbones, including Res2Net50-based, Swin Transformer-based, and PVT V2-based detectors. We consider two conditions: (1) the backbone of the auxiliary generator adopts ResNet50; (2) the backbone of the auxiliary generator aligns with the detector. If the original method is denoted as ICEG, we term the methods optimized in condition 1 and condition 2 as ICEG+ and ICEG++, respectively. Results presented in the table reveal that Camouflageator consistently enhances the performance of these detectors in both conditions, with greater improvement observed when the generator shares the same backbone with the detector. Specifically, Camouflageator improves ICEG by 2.9%, 3.2%, and 4.3% when the backbone of the generator and the detector are Res2Net50, Swin Transformer, and PVT V2. Furthermore, Camouflageator enhances BGNet, a state-of-the-art Res2Net50-based detector, by 2.3% (BGNet+ with ResNet50-based generator) and 2.7% (BGNet++ with Res2Net50-based generator). For the Swin Transformer-based detector, Camouflageator improves ICON by 1.5% (ICON+ with ResNet50-based generator) and 3.7% (ICON++ with Swin Transformer-based generator). Additionally, HitNet, a PVT V2-based detector, exhibits improvements with our Camouflageator, showing an increase of 0.9% (HitNet+ with ResNet50-based generator) and 3.9% (HitNet++ with PVT V2-based generator). 

|Methods||Backbones|*CHAMELEON*||||*CAMO*||||*COD10K*||||*NC4K*||||
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
||||$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|
|BGNet||Res2Net50|0.029|0.835|0.944|0.895|0.073|0.744|0.870|0.812|0.033|0.714|0.901|0.831|0.044|0.786|0.907|0.851|
|BGNet+||Res2Net50|0.027|0.845|0.952|0.898|0.070|0.760|0.882|0.818|0.031|0.729|0.913|0.838|0.042|0.800|0.916|0.860|
|BGNet++ (Res2Net50-based generator)||Res2Net50|0.027|0.847|0.956|0.903|0.070|0.761|0.885|0.820|0.030|0.735|0.918|0.833|0.042|0.805|0.922|0.863|
|ICEG||Res2Net50|0.025|0.869|0.958|0.908|0.066|0.808|0.903|0.838|0.028|0.752|0.914|0.845|0.042|0.828|0.917|0.867|
|ICEG+||Res2Net50|0.023|0.873|0.960|0.910|0.064|0.826|0.912|0.845|0.026|0.770|0.925|0.853|0.040|0.844|0.928|0.878|
|ICEG++ (Res2Net50-based generator)||Res2Net50|0.023|0.876|0.966|0.915|0.063|0.833|0.916|0.849|0.026|0.777|0.930|0.855|0.039|0.847|0.932|0.879|
|ICON||Swin|0.029|0.848|0.940|0.898|0.058|0.794|0.907|0.840|0.033|0.720|0.888|0.818|0.041|0.817|0.916|0.858|
|ICON+||Swin|0.028|0.855|0.943|0.899|0.056|0.808|0.915|0.843|0.031|0.728|0.895|0.822|0.040|0.823|0.922|0.861|
|ICON++ (Swin transformer-based generator)||Swin|0.026|0.862|0.949|0.902|0.055|0.823|0.926|0.849|0.029|0.749|0.906|0.830|0.037|0.836|0.931|0.866|
|ICEG||Swin|0.023|0.860|0.959|0.905|0.044|0.855|0.926|0.867|0.024|0.782|0.930|0.857|0.034|0.855|0.932|0.879|
|ICEG+||Swin|0.022|0.867|0.961|0.908|0.042|0.861|0.931|0.871|0.023|0.788|0.934|0.862|0.033|0.861|0.937|0.883|
|ICEG++ (Swin transformer-based generator)||Swin|0.021|0.883|0.969|0.911|0.041|0.868|0.938|0.876|0.021|0.803|0.945|0.865|0.032|0.869|0.944|0.885|
|HitNet||PVT|0.024|0.861|0.944|0.907|0.060|0.791|0.892|0.834|0.027|0.790|0.922|0.847|0.042|0.825|0.911|0.858|
|HitNet+||PVT|0.023|0.863|0.945|0.909|0.060|0.797|0.895|0.835|0.026|0.794|0.925|0.849|0.041|0.829|0.916|0.860|
|HitNet++ (PVT-based generator)||PVT|0.022|0.879|0.957|0.916|0.057|0.820|0.913|0.839|0.023|0.818|0.936|0.853|0.037|0.849|0.931|0.867|
|ICEG||PVT|0.022|0.879|0.957|0.913|0.043|0.863|0.933|0.876|0.022|0.805|0.938|0.871|0.030|0.869|0.941|0.890|
|ICEG+||PVT|0.022|0.881|0.958|0.915|0.041|0.867|0.935|0.877|0.021|0.811|0.939|0.873|0.029|0.875|0.944|0.893|
|ICEG++ (PVT-based generator)||PVT|0.021|0.889|0.965|0.920|0.039|0.880|0.949|0.881|0.019|0.832|0.968|0.903|0.025|0.896|0.960|0.908|

Reviewer comment (Experiments):
Please provide the visualization maps of module ablations, and consider leveraging CAM-family (activation maps) for better illustration.

Author response:
**Q2: Visualization of module ablations.**

We have included visualization maps of module ablations in Fig. S2 of the supplementary material. To enhance clarity, we have updated this figure using the CAM-family for better illustration. As exhibited in Fig. 2(d), the detector with CFC module can better excavate the internal coherence of camouflaged objects, facilitating the generation of more complete prediction maps. Additionally, as depicted in Fig. 2(e), the network equipped with the ESD component can decrease the uncertainty fringes and thus generate more precise results with clearer boundaries.

Reviewer comment (Experiments):
“CamDiff: Camouflage Image Augmentation via Diffusion Model” also utilizes the generation of camouflaged images to help with COD and should be included to the comparison.

Author response:
**Q1: Difference with CamDiff.**

Idea: The motivation behind CamDiff is to address the challenge of existing COD methods in distinguishing between salient and camouflaged objects. Conversely, our approach focuses on enhancing the discriminative feature extraction capacity of current detectors to improve their overall generalizability. Since the root issue CamDiff aims to resolve is the insufficient discriminative information extraction by existing detectors between salient and camouflaged objects, our strategy can also effectively address this problem.

Implementation: CamDiff generates synthetic salient objects within camouflaged scenes and establishes a **fixed** dataset for detector training. In contrast, our Camouflageator employs a continuously updated generator, synthesizing training data while simultaneously training the detector in an adversarial manner. This approach **tailors** the dataset for each detector, enhancing flexibility and ensuring superior performance.

Experiment: The results presented in the table compare detectors trained with our Camouflageator and CamDiff. Notably, detectors optimized by Camouflageator consistently achieve superior performance. In contrast, detectors optimized by CamDiff occasionally exhibit lower performance than their original versions. This discrepancy may stem from the introduction of salient objects disrupting overall image harmonization during CamDiff's dataset construction. Moreover, the second row of Fig. S2 in the supplementary material illustrates that the original detector, ICEG, encounters challenges in separating salient and camouflaged objects. However, after being optimized by the Camouflageator, ICEG+ adeptly identifies camouflaged objects. This underscores that Camouflageator effectively addresses the problem CamDiff seeks to solve, yielding substantial performance improvements.

|Methods|COD10K||||
|-|-|-|-|-|
||$M$|$F_\beta$|$E_\phi$|$S_\alpha$|
|FGANet|0.032|0.708|0.894|0.803|
|FGANet+CamDiff|0.032|0.711|0.886|0.807|
|FGANet+ (Ours)|0.030|0.735|0.911|0.823|
|FEDER|0.032|0.715|0.892|0.810|
|FEDER+CamDiff|0.033|0.704|0.881|0.805|
|FEDER+ (Ours)|0.030|0.739|0.905|0.831|
|ICEG|0.030|0.747|0.906|0.826|
|ICEG+CamDiff|0.031|0.722|0.898|0.821|
|ICEG+ (Ours)|0.028|0.763|0.920|0.843|

Reviewer comment (Novelty):
There exist many COD methods that are not mentioned in the RELATED WORK: CAMOUFLAGED OBJECT DETECTION part, such as: Mutual graph learning for camouflaged object detection, A Bayesian approach to camouflaged moving object detection, Uncertainty-guided transformer reasoning for camouflaged object detection, and Deep texture-aware features for camouflaged object detection. Although some papers are cited, they need to be reflected in the related work.

Author response:
**Q2: More related works.**

We add the discussions of those papers in the Related Works. Notice that our related work provides a comprehensive categorization of the types of learning-based methods available and that the three learning-based methods can also be classified into these three categories. For instance, MGL [1] falls into the category utilizing a joint training strategy. Similarly, UGTR [2] and DTAF [3] can be classified under the category employing multi-scale feature aggregation.

Reviewer comment (Theory):
Why does Equation 4 use two losses for segmentation.

Author response:
**Q3: Losses used for segmentation.**

It is a common practice to use weighted BCE loss and weighted IoU loss for supervision in the field of COD, and our approach aligns with this convention. Weighted BCE loss treats each pixel as an independent sample for prediction, whereas weighted IoU loss provides a more comprehensive evaluation of the final prediction. The synergy between these two loss functions ensures a thorough assessment of the prediction results.

Reviewer comment (Novelty):
In Sec 3.2.1 Camouflaged consistency loss, what is the difference between the ideas in the paper and the contrastive loss.

Author response:
**Q4: Difference between camouflaged consistency loss and contrastive loss.**

The contrastive loss emphasizes the alignment of positive pairs (and misalignment of negative pairs). As a difference, our camouflaged consistency loss simultaneously encourages **intra-group compactness** and **inter-group separation**. Our loss is particularly designed for camouflaged image pixels. In this context, foreground pixels exhibit a strong intrinsic correlation, while the composition of background pixels tends to be intricate. As a result, our camouflaged consistency loss exclusively constrains the foreground pixels. We aim for the foreground pixels to exhibit compactness among themselves, and we further desire these compact foreground pixels to be distinct from the background pixels. Notably, we intentionally avoid enforcing compactness on background pixels due to their inherent redundancy, as such a requirement could compromise the feature extraction capability.

Reviewer comment (Writing):
In Eq 17, are mu and sigma the same?

Author response:
**Q5: Are $\mu$ and $\sigma$ the same?**

$\mu$ and $\sigma$ are variational parameters and they are different. We reformulate Eq. 17 to avoid misunderstanding.

Reviewer comment (Experiments):
Why was the backbone chosen to be ResNet-50 and not the transformer structure.

Author response:
**Q6: Why resnet50 rather than transformer?**

We choose ResNet50 as the backbone of the auxiliary generator, aligning with common practice in generator design. It's important to note that our Camouflageator framework is not confined to specific models. We conducted a thorough evaluation of Camouflageator's generalizability across generators with diverse backbones, including Res2Net50-based, Swin Transformer-based, and PVT V2-based generators. In exploring the suitability of different backbones for Camouflageator, our experiments focused on two conditions: (1) the auxiliary generator's backbone is ResNet50, and (2) the auxiliary generator shares the same backbone with the detector. Denoting the original method as ICEG, we term the methods optimized in conditions 1 and 2 as ICEG+ and ICEG++, respectively. The results presented in the table consistently demonstrate that Camouflageator enhances the performance of these detectors under both conditions, with more significant improvement observed when the generator shares the same backbone with the detector. Specifically, Camouflageator improves ICEG by 2.9%, 3.2%, and 4.3% when the backbones of the generator and the detector are Res2Net50, Swin Transformer, and PVT V2, respectively. Furthermore, Camouflageator enhances BGNet, a state-of-the-art Res2Net50-based detector, by 2.3% (BGNet+ with ResNet50-based generator) and 2.7% (BGNet++ with Res2Net50-based generator). For the Swin Transformer-based detector, Camouflageator improves ICON by 1.5% (ICON+ with ResNet50-based generator) and 3.7% (ICON++ with Swin Transformer-based generator). Additionally, HitNet, a PVT V2-based detector, shows improvements with our Camouflageator, exhibiting an increase of 0.9% (HitNet+ with ResNet50-based generator) and 3.9% (HitNet++ with PVT V2-based generator).

|Methods||Backbones|*CHAMELEON*||||*CAMO*||||*COD10K*||||*NC4K*||||
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
||||$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|
|BGNet||Res2Net50|0.029|0.835|0.944|0.895|0.073|0.744|0.870|0.812|0.033|0.714|0.901|0.831|0.044|0.786|0.907|0.851|
|BGNet+||Res2Net50|0.027|0.845|0.952|0.898|0.070|0.760|0.882|0.818|0.031|0.729|0.913|0.838|0.042|0.800|0.916|0.860|
|BGNet++ (Res2Net50-based generator)||Res2Net50|0.027|0.847|0.956|0.903|0.070|0.761|0.885|0.820|0.030|0.735|0.918|0.833|0.042|0.805|0.922|0.863|
|ICEG||Res2Net50|0.025|0.869|0.958|0.908|0.066|0.808|0.903|0.838|0.028|0.752|0.914|0.845|0.042|0.828|0.917|0.867|
|ICEG+||Res2Net50|0.023|0.873|0.960|0.910|0.064|0.826|0.912|0.845|0.026|0.770|0.925|0.853|0.040|0.844|0.928|0.878|
|ICEG++ (Res2Net50-based generator)||Res2Net50|0.023|0.876|0.966|0.915|0.063|0.833|0.916|0.849|0.026|0.777|0.930|0.855|0.039|0.847|0.932|0.879|
|ICON||Swin|0.029|0.848|0.940|0.898|0.058|0.794|0.907|0.840|0.033|0.720|0.888|0.818|0.041|0.817|0.916|0.858|
|ICON+||Swin|0.028|0.855|0.943|0.899|0.056|0.808|0.915|0.843|0.031|0.728|0.895|0.822|0.040|0.823|0.922|0.861|
|ICON++ (Swin transformer-based generator)||Swin|0.026|0.862|0.949|0.902|0.055|0.823|0.926|0.849|0.029|0.749|0.906|0.830|0.037|0.836|0.931|0.866|
|ICEG||Swin|0.023|0.860|0.959|0.905|0.044|0.855|0.926|0.867|0.024|0.782|0.930|0.857|0.034|0.855|0.932|0.879|
|ICEG+||Swin|0.022|0.867|0.961|0.908|0.042|0.861|0.931|0.871|0.023|0.788|0.934|0.862|0.033|0.861|0.937|0.883|
|ICEG++ (Swin transformer-based generator)||Swin|0.021|0.883|0.969|0.911|0.041|0.868|0.938|0.876|0.021|0.803|0.945|0.865|0.032|0.869|0.944|0.885|
|HitNet||PVT|0.024|0.861|0.944|0.907|0.060|0.791|0.892|0.834|0.027|0.790|0.922|0.847|0.042|0.825|0.911|0.858|
|HitNet+||PVT|0.023|0.863|0.945|0.909|0.060|0.797|0.895|0.835|0.026|0.794|0.925|0.849|0.041|0.829|0.916|0.860|
|HitNet++ (PVT-based generator)||PVT|0.022|0.879|0.957|0.916|0.057|0.820|0.913|0.839|0.023|0.818|0.936|0.853|0.037|0.849|0.931|0.867|
|ICEG||PVT|0.022|0.879|0.957|0.913|0.043|0.863|0.933|0.876|0.022|0.805|0.938|0.871|0.030|0.869|0.941|0.890|
|ICEG+||PVT|0.022|0.881|0.958|0.915|0.041|0.867|0.935|0.877|0.021|0.811|0.939|0.873|0.029|0.875|0.944|0.893|
|ICEG++ (PVT-based generator)||PVT|0.021|0.889|0.965|0.920|0.039|0.880|0.949|0.881|0.019|0.832|0.968|0.903|0.025|0.896|0.960|0.908|

Reviewer comment (Presentation):
The paper presents two significant contributions: firstly, the introduction of an adversarial training framework for COD tasks, and secondly, the implementation of ICEG to overcome the limitations of incomplete segmentation and ambiguous boundaries for camouflaged objects. However, the two contributions seem disconnected, and they appear to be independent approaches aimed at enhancing network segmentation without a clear connection between them. Consequently, the primary storyline of the work is missing. The authors must establish the relationship between the two contributions and articulate how they work together to achieve the overall objective of the paper.

Author response:
**Q1: Missing primary storyline and lacking the establishment of the relationship between Camouflageator and ICEG.**

As stated in the third paragraph of the Introduction, our method is inspired by the prey-vs-predator game, which leads to ever-strategic preys and ever-acute predators, and we propose to address COD by developing algorithms on both the prey side (Camouflageator) that generates more deceptive camouflage objects and the predator side (ICEG) that produces complete and precise detection results. Therefore, Camouflageator and ICEG are complementary and can be combined.

In Section 3.2.4, we present how to integrate the Camouflageator framework with ICEG and denote the integrated method as ICEG+. As depicted in Fig. 1 in the manuscript, ICEG+ successfully achieves the overarching objectives of our paper and excels in addressing the extreme challenges in COD, including false localization, incomplete prediction, and ambiguous boundaries.

In the updated manuscript, we have revised the Introduction to make it clearer about the relationship between Camouflageator and ICEG. Thank you for pointing it out.

Reviewer comment (Novelty):
The adversarial training framework, which represents the primary contribution of this paper, is not a novel concept. Although it has yet to be implemented in COD tasks, the fundamental idea and implementation are similar to other tasks, such as image deblurring. It would be beneficial to underscore the unique difficulties and differences between COD and other tasks if the implementation is non-trivial.

Author response:
**Q2: Difference of Camouflageator between the adversarial training framework in other tasks.**

Our method diverges significantly from practices employed in other tasks, such as image deblurring, both in fundamental idea and implementation.

Fundamental idea: Existing methods, such as image deblurring methods [1,2,3], typically utilize adversarial training strategies, employing a classification-based discriminator to **ensure the generation of visually faithful results**. The focus lies on the **generator** side.  In contrast, drawing inspiration from the prey-vs-predator game, we introduce a task-oriented framework, Camouflageator, for COD. The primary objective is to generate more deceptive camouflage objects, thereby **enhancing the generalizability of the detector** (termed task-oriented). Our focus lies on the **detector** (or discriminator) side. 

Implementation: To achieve the goal of deception, we introduce the concealment loss (Eq. 3) for generating deceptive objects, a concept not previously proposed in existing adversarial training methods. Unlike purely data-driven generators (task-agnostic), our auxiliary generator is partially supervised by the detector (Eq. 4) in a task-wise manner. Additionally, in contrast to prevailing adversarial training strategies, we deliberately modify the network and omit the discriminator (refer to Table S11 in the supplementary material). This choice which is deliberate as the objective of our Camouflageator framework is to bolster the generalizability of the detector, not to ensure complete unbiasedness of the synthesized images' distribution from the original camouflaged data. Overemphasis on the latter aspect may even lead to a degradation of detection performance.

[1] Deblurgan, CVPR18.

[2] RWB, CVPR20.

[3] SAMD, TIP.

Reviewer comment (Experiments):
It is imperative to ascertain the efficacy of the proposed adversarial training framework for other state-of-the-art (SOTA) networks by conducting ablations of the framework with alternative methods. This endeavor would furnish evidence to substantiate the generalizability of the proposed approach and its applicability to other networks.

Author response:
**Q4: Verify the generalization of the proposed Camouflageator.**

We have proved the generalizability of our Camouflageator, whose generator employs ResNet50 as the backbone, on several cutting-edge ResNet50-based detectors, including PreyNet, FGANet, and FEDER, in Table 1 in the manuscript. Camouflageator consistently enhances other detectors by 2.8% (PreyNet), 2.2% (FGANet), 2.3% (FEDER). Besides, Table S4 in the supplementary material illustrates that the network trained with Camouflageator excels in handling extreme conditions, such as small objects and multiple objects.

We further assess the generalizability of Camouflageator on detectors with various backbones, including Res2Net50-based, Swin Transformer-based, and PVT V2-based detectors. We consider two conditions: (1) the backbone of the auxiliary generator adopts ResNet50; (2) the backbone of the auxiliary generator aligns with the detector. If the original method is denoted as ICEG, we term the methods optimized in condition 1 and condition 2 as ICEG+ and ICEG++, respectively. Results presented in the table reveal that Camouflageator consistently enhances the performance of these detectors in both conditions, with greater improvement observed when the generator shares the same backbone with the detector. Specifically, Camouflageator improves ICEG by 2.9%, 3.2%, and 4.3% when the backbone of the generator and the detector are Res2Net50, Swin Transformer, and PVT V2. Furthermore, Camouflageator enhances BGNet, a state-of-the-art Res2Net50-based detector, by 2.3% (BGNet+ with ResNet50-based generator) and 2.7% (BGNet++ with Res2Net50-based generator). For the Swin Transformer-based detector, Camouflageator improves ICON by 1.5% (ICON+ with ResNet50-based generator) and 3.7% (ICON++ with Swin Transformer-based generator). Additionally, HitNet, a PVT V2-based detector, exhibits improvements with our Camouflageator, showing an increase of 0.9% (HitNet+ with ResNet50-based generator) and 3.9% (HitNet++ with PVT V2-based generator).

|Methods||Backbones|*CHAMELEON*||||*CAMO*||||*COD10K*||||*NC4K*||||
|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
||||$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|$M$|$F_\beta$|$E_\phi$|$S_\alpha$|
|BGNet||Res2Net50|0.029|0.835|0.944|0.895|0.073|0.744|0.870|0.812|0.033|0.714|0.901|0.831|0.044|0.786|0.907|0.851|
|BGNet+||Res2Net50|0.027|0.845|0.952|0.898|0.070|0.760|0.882|0.818|0.031|0.729|0.913|0.838|0.042|0.800|0.916|0.860|
|BGNet++ (Res2Net50-based generator)||Res2Net50|0.027|0.847|0.956|0.903|0.070|0.761|0.885|0.820|0.030|0.735|0.918|0.833|0.042|0.805|0.922|0.863|
|ICEG||Res2Net50|0.025|0.869|0.958|0.908|0.066|0.808|0.903|0.838|0.028|0.752|0.914|0.845|0.042|0.828|0.917|0.867|
|ICEG+||Res2Net50|0.023|0.873|0.960|0.910|0.064|0.826|0.912|0.845|0.026|0.770|0.925|0.853|0.040|0.844|0.928|0.878|
|ICEG++ (Res2Net50-based generator)||Res2Net50|0.023|0.876|0.966|0.915|0.063|0.833|0.916|0.849|0.026|0.777|0.930|0.855|0.039|0.847|0.932|0.879|
|ICON||Swin|0.029|0.848|0.940|0.898|0.058|0.794|0.907|0.840|0.033|0.720|0.888|0.818|0.041|0.817|0.916|0.858|
|ICON+||Swin|0.028|0.855|0.943|0.899|0.056|0.808|0.915|0.843|0.031|0.728|0.895|0.822|0.040|0.823|0.922|0.861|
|ICON++ (Swin transformer-based generator)||Swin|0.026|0.862|0.949|0.902|0.055|0.823|0.926|0.849|0.029|0.749|0.906|0.830|0.037|0.836|0.931|0.866|
|ICEG||Swin|0.023|0.860|0.959|0.905|0.044|0.855|0.926|0.867|0.024|0.782|0.930|0.857|0.034|0.855|0.932|0.879|
|ICEG+||Swin|0.022|0.867|0.961|0.908|0.042|0.861|0.931|0.871|0.023|0.788|0.934|0.862|0.033|0.861|0.937|0.883|
|ICEG++ (Swin transformer-based generator)||Swin|0.021|0.883|0.969|0.911|0.041|0.868|0.938|0.876|0.021|0.803|0.945|0.865|0.032|0.869|0.944|0.885|
|HitNet||PVT|0.024|0.861|0.944|0.907|0.060|0.791|0.892|0.834|0.027|0.790|0.922|0.847|0.042|0.825|0.911|0.858|
|HitNet+||PVT|0.023|0.863|0.945|0.909|0.060|0.797|0.895|0.835|0.026|0.794|0.925|0.849|0.041|0.829|0.916|0.860|
|HitNet++ (PVT-based generator)||PVT|0.022|0.879|0.957|0.916|0.057|0.820|0.913|0.839|0.023|0.818|0.936|0.853|0.037|0.849|0.931|0.867|
|ICEG||PVT|0.022|0.879|0.957|0.913|0.043|0.863|0.933|0.876|0.022|0.805|0.938|0.871|0.030|0.869|0.941|0.890|
|ICEG+||PVT|0.022|0.881|0.958|0.915|0.041|0.867|0.935|0.877|0.021|0.811|0.939|0.873|0.029|0.875|0.944|0.893|
|ICEG++ (PVT-based generator)||PVT|0.021|0.889|0.965|0.920|0.039|0.880|0.949|0.881|0.019|0.832|0.968|0.903|0.025|0.896|0.960|0.908|