NIH Toolbox Cognitive Battery Overview

• NIH Toolbox Cognitive Battery is a set of standardized, computer-based tests that measure various cognitive functions such as memory, attention, and executive function.
• It provides reliable, quantitative metrics for key domains, enabling large-scale studies and facilitating comparisons across populations.
• It supports age-spanning assessments with flexible administration protocols and has been validated through extensive normative research.

Below is a self‐contained summary of the “Diffusion Attack” paper, focused on the “DiffusionAttacker” pipeline, its mathematical underpinnings, optimization, empirical performance, and limitations.

1. Method Overview DiffusionAttacker is built in three stages:
   1. Text-driven Style Generation via Stable Diffusion
      • We feed a natural‐language prompt (e.g. “spray‐paint pattern on T-shirt”) into a frozen, pre-trained Stable Diffusion model (encoder + U-Net denoiser + decoder).
      • The model produces a “style image” S that exhibits the requested texture, color palette, and high-level semantics.
   2. Neural Style Transfer onto the Content Image
      • Given a clean content image x (e.g. a photo of a T-shirt), we perform neural style transfer conditioned on S.
      • A mask may optionally restrict stylization to an object region (e.g. the shirt).
      • This produces xₛₜ, which retains object geometry and high-level content but now carries the diffusion-generated style.
   3. Adversarial Attack Refinement
      • We take xₛₜ as the starting point for a goal-oriented image attack on a fixed target classifier f(·) (e.g. Inception-v3).
      • By iteratively back-propagating the classification (cross-entropy) loss and a smoothness regularizer, we push f(x′) → yₜₐᵣgₑₜ, producing a final attacked image x*.
      • The complete “Diffusion Attack” thus yields a naturalistic yet highly effective adversarial example.

2. Mathematical Formulation DiffusionAttacker minimizes a weighted sum of four losses: content, style, adversarial, and smoothness. Let x′ denote the current generated image.

• Content Loss (preserve shape and layout) L₍cₒₙₜ₎(x′) = ∥Φ_c(x′) – Φ_c(x)∥₂² where Φ_c(·) is the feature map at a deep convolutional layer (e.g. VGG‐19 relu_4_2).
• Style Loss (match style correlations) L₍sₜyₗₑ₎(x′) = Σ_{l∈ℓ} ∥G(Φ_l(x′)) – G(Φ_l(S))∥₂² where G(F)=F·F^T (the Gram matrix) and ℓ indexes several intermediate layers.
• Adversarial Loss (drive misclassification) L₍ᵃᵈᵥ₎(x′) = – log p_{f}(yₜₐᵣgₑₜ | x′) (equivalently the cross-entropy between f(x′) and one-hot target yₜₐᵣgₑₜ).
• Smoothness Loss (encourage natural local continuity) L₍ₛₘₒₒₜₕ₎(x′) = Σ{i,j} ∥x′{i,j} – x′{i+1,j}∥₂² + ∥x′{i,j} – x′_{i,j+1}∥₂².

The total loss is Lₜₒₜₐₗ(x′) = λ₁ L₍cₒₙₜ₎(x′) + λ₂ L₍sₜyₗₑ₎(x′) + λ₃ L₍ᵃᵈᵥ₎(x′) + λ₄ L₍ₛₘₒₒₜₕ₎(x′).

In a constrained‐norm formulation one could write: x* = arg min_{x′} Lₜₒₜₐₗ(x′) s.t. ∥x′ – x∥ₚ ≤ ε. In practice the paper does not explicitly enforce a hard ε‐ball but relies on small λ₃ and λ₄ to keep perturbations imperceptible.

1. Optimization and Implementation Details
   • Two‐stage training:
     • Stage 1 (Style Transfer): optimize L₍cₒₙₜ₎+L₍sₜyₗₑ₎ by L-BFGS (lr=1.0, batch=4) until xₛₜ converges.
     • Stage 2 (Adversarial Attack): fix the style transfer network, fine-tune x′ with SGD (or Adam) on Lₜₒₜₐₗ.

• Back-propagation through the diffusion model is not used; we only back-prop through the style‐transfer CNN and the classifier. The diffusion model is a frozen generator for styles.
• Pseudo‐code for generating a DiffusionAttacker example:

  Input: clean image x, text prompt T, target label y_t 1. S ← StableDiffusion.generate(T) 2. x_st ← StyleTransferNetwork(x, S) # minimize L_content + L_style 3. x' ← x_st 4. repeat ∇ ← ∂/∂x' [ λ1 L_content(x') + λ2 L_style(x') + λ3 L_adv(x') + λ4 L_smooth(x') ] x' ← x' – η · normalize(∇) until f(x') predicts y_t with high confidence 5. return x* = x'

1. Experimental Results (a) Perceptual Quality (non‐reference metrics, higher=better):
   • NIMA DiffusionAttack=4.78 | Content=5.37 | SLAPs=3.73 | Woitschek=2.97
   • Topiq_iaa DiffusionAttack=4.46 | …
   • Topiq_nr DiffusionAttack=0.62
   • Tres DiffusionAttack=72.32

(b) Attack Success Rates: * T-shirt → umbrella/lighthouse with ≥93% confidence * Backpack → sleeping bag/zebra with ≈90% confidence

(c) Ablation on λ’s and diffusion steps: The authors report qualitatively that increasing λ₂ (style) yields more texture fidelity but may slow convergence of the adversarial loss; increasing λ₃ (adv) speeds misclassification but sometimes artifacts increase. No full grid study was provided.

1. Discussion and Limitations Strengths:
   • Natural‐looking perturbations drawn from a rich text‐to‐image prior.
   • High perceptual quality by non‐reference metrics (NIMA, Topiq, Tres).
   • Maintains strong targeted‐attack performance (> 90% success).

Limitations: * Two‐stage optimization (L-BFGS + iterative attack) is computationally heavy. * No explicit ℓₚ constraint means worst‐case distortions may become visible if λ₃ is too large. * The attack is white-box with access to the classifier and style network—black-box transferability remains untested. * No large‐scale study on robustness to defenses (e.g. adversarial training).

In summary, DiffusionAttacker tightly couples text-driven diffusion‐model style generation with classical neural style transfer and a cross‐entropy adversarial objective. By weaving these losses,

$$x^* = \arg\min_{x'} \bigl[\,\lambda_1 L_{\rm content}(x') \;+\;\lambda_2 L_{\rm style}(x') \;+\;\lambda_3 L_{\rm adv}(x') \;+\;\lambda_4 L_{\rm smooth}(x')\bigr],$$

the method yields high‐quality, naturalistic images that successfully fool a target DNN with high confidence.