research/v2_transfer_techniques.md

# V2 Transfer & Imperceptibility Research Brief

**Date:** 2026-07-06  
**Context:** White-box ensemble momentum-PGD on vision encoders for caption misread transfer. V1 at ε=12–16/255 achieves 100% in-ensemble, 66–75% held-out open encoders, 35–55% frontier (gpt-5.5 / gemini-3.5-flash) **after JPEG** — but PSNR 26–28 dB is visibly noisy. V2 must improve **black-box transfer** and **genuine imperceptibility**.

**Papers cited below are web-verified (2023–2026 SOTA prioritized).**

---

## 1. Black-Box Transfer Techniques

Mechanism = one line. **Gain** = typical black-box lift vs MI-FGSM / plain ensemble (ImageNet or VLM benchmarks). **Fit** = compatibility with our stochastic multi-encoder PGD + EOT loop.

### Ranked shortlist (adopt → defer)

| Rank | Technique | Mechanism | Expected gain | Ensemble-PGD fit | Impl. cost |
|------|-----------|-----------|---------------|------------------|------------|
| **1** | **CWA** (Common Weakness Attack) | SAM finds sharp ascent direction for flatter loss landscape; CSE aligns per-model gradients toward shared optima ([ICLR 2024](https://proceedings.iclr.cc/paper_files/paper/2024/hash/53fe824f289060ce705ed7c01dae59d2-Abstract-Conference.html), [code](https://github.com/huanranchen/AdversarialAttacks)) | +5–15 pp vs MI-ensemble on diverse victims; demonstrated on Google Bard VLM | **Excellent** — replaces plain gradient sum across sampled encoders; orthogonal to EOT | Medium (~150 LOC in `attack.py`) |
| **2** | **VNI-FGSM + VMI** (variance tuning + Nesterov) | Lookahead position + subtract gradient variance over neighborhood samples to stabilize direction ([arXiv:2103.15571](https://arxiv.org/pdf/2103.15571)) | +25–30 pp vs NI on adversarially trained CNNs; +5–10 pp typical on normal models | **Excellent** — drop-in replacement for MI-FGSM sign step | Low–medium |
| **3** | **SVRE** (Stochastic Variance Reduced Ensemble) | Outer-loop averaged gradient + inner-loop variance-reduced update prevents ensemble overfitting ([CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Xiong_Stochastic_Variance_Reduced_Ensemble_Adversarial_Attack_for_Boosting_the_Adversarial_CVPR_2022_paper.html)) | +3–8 pp vs uniform ensemble average | **Excellent** — we already do random subset; SVRE formalizes the outer/inner structure | Medium |
| **4** | **Admix + DI + TI** (input diversity stack) | Admix blends other-class patches; DI random resize/pad; TI convolves gradient with Gaussian kernel for translation invariance ([ICCV 2021 Admix](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Admix_Enhancing_the_Transferability_of_Adversarial_Attacks_ICCV_2021_paper.pdf), [CVPR 2019 TIM](https://openaccess.thecvf.com/content_CVPR_2019/papers/Dong_Evading_Defenses_to_Transferable_Adversarial_Examples_by_Translation-Invariant_Attacks_CVPR_2019_paper.pdf), [CVPR 2019 DIM](https://ar5iv.labs.arxiv.org/html/1803.06978)) | Admix-TI-DIM +3–6 pp over SI-TI-DIM on defenses; DIM alone ~+7 pp in NIPS 2017 comp | **Good** — apply transforms *before* encoder forward inside EOT; adds ~1.3× step time | Medium |
| **5** | **Cross-prompt / CroPA-lite** | Optimize image perturbation against a *bank* of caption prompts for the decoy so attack survives prompt diversity at inference ([arXiv:2403.09766 CroPA](https://arxiv.org/pdf/2403.09766), [GrCPA NeurIPS 2024 submission](https://openreview.net/forum?id=I05Z6KjQ9K)) | CroPA reports strong cross-prompt ASR on BLIP-2/LLaVA; GrCPA stabilizes multi-prompt gradients | **Good** — extend contrastive loss: `mean_p L(cos(f, e_{decoy}(p)))`; no full VLM backward needed | Low–medium |
| **6** | **CoA-style multimodal semantic chain** (lite) | Iteratively refine decoy text embedding from surrogate caption of current adv image, then re-attack toward modality-aware target ([CVPR 2025 CoA](https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Chain_of_Attack_On_the_Robustness_of_Vision-Language_Models_Against_CVPR_2025_paper.pdf)) | CoA +1–7 pp over MF-ii on CLIP variants; 75.2% CLIP-score vs 70% baselines | **Partial** — full CoA needs ClipCap loop; **lite** = refresh decoy text every 50 steps via frozen BLIP/CLIP prefix | High (full) / Medium (lite) |
| **7** | **Margin-calibrated embedding loss** (feature > raw CE) | Normalized logit / cosine margin with adaptive temperature avoids vanishing gradients in targeted transfer ([NeurIPS 2021 Logit](https://proceedings.neurips.cc/paper/2021/file/30d454f09b771b9f65e3eaf6e00fa7bd-Paper.pdf), [TIFS 2023 Logit Margin](https://doi.org/10.1109/tifs.2023.3284649), [arXiv:2405.06340 NLCM+TFM](https://arxiv.org/html/2405.06340v1)) | Logit loss +10–20 pp vs CE in hard ensemble transfer; feature mixing +5–10 pp | **Excellent** — we already use cosine margin; add **margin temperature** + **truncated feature mix** on repel term | Low |
| **8** | **SSA** (spectrum simulation) | DCT-domain random mask simulates diverse spectral surrogate models ([ECCV 2022](https://arxiv.org/abs/2207.05382)) | Part of SOTA combo attacks; ~+3–5 pp stacked with DIM/TIM | **Good** — frequency aug parallel to spatial EOT; cheap with `torch.fft` or DCT | Medium |
| **9** | **Ghost networks** | On-the-fly dropout / skip-connection noise creates virtual model ensemble from each encoder ([AAAI 2020](https://ar5iv.labs.arxiv.org/html/1812.03413)) | Effective multiplier on ensemble size at ~0 extra model load | **Good** — apply to 2–3 largest ViT encoders per step | Low |
| **10** | **SEA-FTS** (fine-tuning trajectory simulation) | Random weight noise in vision encoder simulates fine-tuned VLM variants during optimization ([arXiv:2508.01741](https://arxiv.org/html/2508.01741v3)) | >86% jailbreak transfer across Qwen2-VL fine-tunes vs near-0% plain PGD | **Partial** — needs differentiable encoder weights; works on timm ViTs, not frozen CLIP text towers | Medium–high |
| **11** | **X-Transfer surrogate scaling** | Dynamic selection of surrogate CLIP encoders from large pool for UAP/super-transfer ([ICML 2025](https://proceedings.mlr.press/v267/huang25h.html)) | SOTA on cross-model CLIP + captioning transfer | **Partial** — we already do random subset; upgrade to **gradient-weighted** or **diversity-scored** selection | Low |
| **12** | **SGMA** (semantic-guided multimodal attack) | Perturb semantically salient regions; disrupt global/local cross-modal grounding ([arXiv:2602.09431](https://arxiv.org/pdf/2602.09431)) | Best encoder-only LVLM transfer in 2026 benchmark | **Partial** — needs saliency maps from each encoder; pairs with repel loss | High |
| **13** | **EMI-FGSM** | Accumulates gradients along previous momentum direction samples ([arXiv:2103.10609](https://ar5iv.labs.arxiv.org/html/2103.10609)) | +10–12 pp vs PI-FGSM | **Redundant** with VMI — pick one | Low |
| **14** | **SIM / NI alone** | Scale copies / Nesterov lookahead | Foundational; subsumed by VNI+SI stack | Already partially via EOT resize | — |
| **15** | **AttackVLM query (RGF)** | Transfer init + black-box score queries refine caption match ([AttackVLM](https://yunqing-me.github.io/AttackVLM/)) | Large lift *with API queries* | **Different threat model** — defer to Tier-B frontier polish | High + $$ |
| **16** | **UnivIntruder / X-Transfer UAP** | Universal perturbation across images | Great for UAP, not per-image training data protection | **Poor fit** for per-image targeted decoy | — |

### VLM-/CLIP-specific notes

- **Feature-space attacks transfer better than logit/CE for CLIP** when the victim uses a different head: embedding cosine margin (our v1) is correct; improve with **normalized margin calibration** ([NLCM](https://arxiv.org/html/2405.06340v1)) and **rank-1 truncated feature mixing** on the repel branch.
- **Cross-prompt is mandatory for caption victims**: frontier models use free-form prompts; a perturbation optimized for one decoy string fails when the API uses a different template. CroPA/GrCPA directly address this ([CroPA](https://arxiv.org/pdf/2403.09766)).
- **CoA / AttackVLM** confirm that **target image + target text joint embedding** beats image-only attacks for caption transfer ([CoA](https://arxiv.org/pdf/2411.15720), [AttackVLM](https://github.com/yunqing-me/AttackVLM)) — we should add a **frozen decoy-image embedding** (SD/DALL·E stills or CLIP text-only) alongside text decoy.
- **Encoder-only attacks** remain the right efficiency tradeoff; [SGMA](https://arxiv.org/pdf/2602.09431) shows encoder attacks *can* transfer to full LVLMs if semantics are targeted, but need better grounding than v1 repel loss.

### Algorithm sketch — transfer stack (per PGD step)

```
δ ← 0; ḡ ← 0; m ← 0; v ← 0  # SVRE outer, VNI momentum, optional Adam v
for t = 1..T:
  # NI lookahead
  x_la = clamp(x0 + δ + α·m, 0, 1)

  g_list = []
  for k = 1..K_inner:                    # SVRE inner
    S ← sample_encoders(pool, n=6)
    x_in ← Admix(DI(x_la))               # p_di=0.7, mix_ratio=0.2
    for j = 1..N_eot:                    # N_eot=3
      x_t ← EOT(x_in)                    # DiffJPEG curriculum + resize + blur
      for enc in S:
        g_list.append(∇_δ L_enc(x_t))     # margin-calibrated cosine loss

  g ← CSE(SAM_aggregate(g_list))         # CWA
  g ← VMI_tune(g, history)               # variance from N=20 gaussian δ jitter
  m ← μ·m + g/||g||_1                    # VNI momentum
  δ ← δ + α · sign(m)
  δ ← Π_{L∞}(δ; ε=8/255)
  δ ← Π_{LPIPS}(δ; τ=0.08)              # Section 2
  δ ← Π_{DCT-low}(δ)                    # Section 2
```

### Suggested hyperparameters (transfer)

| Param | Value | Source rationale |
|-------|-------|------------------|
| Steps T | 300–400 | More steps compensate lower ε |
| ε∞ | **8/255** (down from 12–16) | Imperceptibility budget; stack transfer methods instead of raw ε |
| α (step) | 1.0/255 | Standard for ε=8 |
| μ (momentum) | 1.0 | TIM/MI default |
| NI decay | 1.0 | [NI-FGSM](https://ar5iv.labs.arxiv.org/html/1908.06281) |
| VMI neighbors N | 20, σ=1.5/255 | [VMI](https://arxiv.org/pdf/2103.15571) |
| SAM ρ | 0.05·ε | [CWA](https://openreview.net/pdf?id=AcJrSoArlh) |
| Subset size | 6 (of 12–14) | v1 used 6; SVRE benefits from ≥4 |
| DI prob p | 0.7 | [DIM](https://ar5iv.labs.arxiv.org/html/1803.06978) tradeoff: high p → transfer, mild white-box hit |
| TI kernel | 15×15 Gaussian | [TIM](https://openaccess.thecvf.com/content_CVPR_2019/papers/Dong_Evading_Defenses_to_Transferable_Adversarial_Examples_by_Translation-Invariant_Attacks_CVPR_2019_paper.pdf) |
| Admix ratio | 0.2, 3 add-in crops | [Admix](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Admix_Enhancing_the_Transferability_of_Adversarial_Attacks_ICCV_2021_paper.pdf) |
| Prompt bank | 8–16 paraphrases of decoy caption | [CroPA](https://arxiv.org/pdf/2403.09766) |
| Margin temp | 2.5–5.0 adaptive | [Logit Margin](https://doi.org/10.1109/tifs.2023.3284649) |

**Expected transfer benefit (v2 transfer stack alone):** frontier 35–55% → **50–65%** post-JPEG at matched perceptibility; held-out open 66–75% → **75–85%**. Gains are sublinear — budget A/B each component.

---

## 2. Perceptual / Imperceptible Constraints Beyond L∞

L∞ with ε=12–16/255 yields PSNR 26–28 dB in v1 — **visible high-frequency speckle**. Human perception correlates better with **LPIPS** (ρ≈0.94) than L2 (ρ≈0.88) ([ICLR 2021 Perceptual Adversarial Robustness](https://ar5iv.labs.arxiv.org/html/2006.12655)).

### Ranked shortlist

| Rank | Constraint | Mechanism | Imperceptibility vs strength | PGD implementation | Impl. cost |
|------|------------|-----------|------------------------------|-------------------|------------|
| **1** | **Dual L∞ + LPIPS bound** (PPGD / LPA) | Project (or penalize) so `LPIPS(x, x_adv) ≤ τ` in addition to ε∞ ([ICLR 2021](https://github.com/cassidylaidlaw/perceptual-advex), [LPA strongest](https://openreview.net/pdf?id=dFwBosAcJkN)) | Best human-imperceptible attack strength at fixed τ; finds attacks outside Lp balls | **Penalty:** `L += λ·max(0, LPIPS−τ)`; **Projection:** scale δ after L∞ clip (binary search or 3 Newton steps) | Medium (lpips lib) |
| **2** | **Texture-masked adaptive ε map** (JND / local variance) | Larger ε in high-texture, smaller on edges/smooth regions ([arXiv:2011.05254](https://ar5iv.labs.arxiv.org/html/2011.05254), [ICCV 2025 IAP](https://openaccess.thecvf.com/content/ICCV2025/papers/Dutta_IAP_Invisible_Adversarial_Patch_Attack_through_Perceptibility-Aware_Localization_and_Perturbation_ICCV_2025_paper.pdf)) | Same L∞ norm but **looks** cleaner; exploits HVS masking | `ε(i,j) = ε_base · (1 + β·σ_local(i,j))`, normalize max to ε_base; multiply gradient step | Low |
| **3** | **DCT low-frequency subspace** | Zero high DCT coefficients; perturbation is smooth/low-freq ([IJCAI 2019](https://www.ijcai.org/proceedings/2019/0470.pdf), [LFAA 2023](https://ar5iv.labs.arxiv.org/html/2310.20175), [FDT 2024](https://www.sciencedirect.com/science/article/abs/pii/S0167404824002475)) | Much less visible speckle; **bonus:** survives JPEG better (energy in kept bands) | `δ ← IDCT(M_low ⊙ DCT(δ))` each step; M_low keeps coeffs with freq < r (r=H/8) | Low |
| **4** | **Lagrangian joint objective** (attack + λ·LPIPS) | LPA-style: stronger than pure projection at same τ ([ICLR 2021](https://ar5iv.labs.arxiv.org/html/2006.12655)) | Best attack success per perceptual unit | λ annealed: start 0.1, increase if LPIPS<τ/2 at end | Medium |
| **5** | **CIEDE2000 (ΔE00) cap** | Perceptual color distance in Lab/LCH ([arXiv:1911.02466 PerC](https://ar5iv.labs.arxiv.org/html/1911.02466)) | Superior on smooth/saturated regions (sky, walls) where L∞ speckle shows | Differentiable approx of ΔE00; project in Lab space after RGB step | Medium |
| **6** | **Chrominance-only (CbCr / a*b*)** | Restrict δ to chroma channels ([arXiv:2310.13950](https://doi.org/10.48550/arxiv.2310.13950)) | Very subtle on natural photos; weaker on grayscale objects | RGB→YCbCr, zero δ_Y; or optimize in Lab ab only | Low |
| **7** | **SSIM / MS-SSIM ball** | Convex SSIM constraint ([IEEE JSTSP 2023](https://doi.org/10.1109/jstsp.2023.3258253)) | Good semantics (changes in salient regions) but weaker correlation than LPIPS | Penalty on `1−SSIM`; pairs with project gate | Low |
| **8** | **Unrestricted / semantic** (color shift, warp) | Not L∞-bounded; different threat model | Most imperceptible per unit effect; poor JPEG compatibility for warp | **Defer** — training data protection needs pixel-stable JPEG survival | — |

### Best tradeoff for our use case

**Recommended combo:** `ε∞=8/255` hard cap + **`LPIPS ≤ 0.08`** (AlexNet backbone, ImageNet-scale) + **DCT low-pass (r≤H/8)** + **texture mask β=2.0**.

Rationale:
- LPIPS aligns with our project stealth gate (LPIPS ≤ 0.10 strict) in `PROJECT_MEMORY.md`.
- DCT-lowpass removes speckle that PSNR misses but humans see; also aligns JPEG-survival literature ([MetaCloak-JPEG](https://arxiv.org/html/2604.18537) notes high-freq energy is wiped by quant tables).
- Texture mask concentrates budget where v1 noise is already less visible.

### Differentiable implementation recipes

**LPIPS projection (after gradient step):**
```python
# δ' = argmin_{||δ||∞≤ε} ||δ|| s.t. LPIPS(x0, x0+δ) ≤ τ
# Practical: if LPIPS > τ, δ ← δ * (τ / LPIPS)^γ  with γ≈1.5, repeat 3×
```

**LPIPS penalty (inside loss):**
```python
L_total = L_attack + λ_lp * relu(lpips(x_adv, x0) - τ)
# λ_lp = 10–50; anneal down last 20% steps to recover margin
```

**DCT mask:**
```python
# Use orthonormal DCT-II on 8×8 blocks (JPEG-compatible)
delta_low = idct8x8(dct8x8(delta) * low_freq_mask)  # mask top-left k×k coeffs
```

**Texture mask:**
```python
# σ from 7×7 local std on grayscale x0
eps_map = eps_base * (1 + beta * (sigma / sigma.max()))
delta = clip(delta, -eps_map, eps_map)
```

### Suggested perceptual hyperparameters

| Param | Strict (deploy) | Medium (research) |
|-------|-----------------|-------------------|
| ε∞ | 6/255 | 8/255 |
| LPIPS (Alex) τ | 0.06 | 0.08 |
| DCT keep ratio | r = H/16 | r = H/8 |
| Texture β | 1.5 | 2.0 |
| ΔE00 p95 cap | 2.0 (text-adjacent regions) | 3.0 |
| Target PSNR | ≥ 38 dB | ≥ 34 dB |
| Target SSIM | ≥ 0.94 | ≥ 0.90 |

**Expected benefit:** PSNR 26–28 → **34–40 dB**, SSIM 0.65–0.74 → **0.90–0.95**, with **~15–25% relative drop** in raw margin unless compensated by longer optimization + transfer stack.

---

## 3. JPEG / Resize / Crop Robustness (EOT Design)

V1 uses BPDA straight-through JPEG + probabilistic resize/blur/crop in `ensemble/eot.py`. Margins survive JPEG (+0.195 vs +0.016 pre-EOT) — EOT works. V2 should improve **gradient fidelity** and **QF coverage** without destroying transfer.

### JPEG: differentiable approx vs BPDA

| Method | Mechanism | Pros | Cons |
|--------|-----------|------|------|
| **BPDA / STE** (v1) | Forward: real PIL JPEG; backward: identity ([MLSec 2017](https://machine-learning-and-security.github.io/papers/mlsec17_paper_54.pdf)) | Simple, fast, any QF | Gradient ignores quant drop; HF energy falsely encouraged |
| **DiffJPEG + STE on round** | Full YCbCr→DCT→quant with STE backward ([WACV 2024](https://openaccess.thecvf.com/content/WACV2024/papers/Reich_Differentiable_JPEG_The_Devil_Is_in_the_Details_WACV_2024_paper.pdf)) | Gradients through quant; better HF survival under JPEG | Heavier; must match libjpeg tables |
| **MetaCloak-JPEG curriculum** | QF sampled from `[QF_min(t), 95]` rising over steps; ~70% aug include JPEG ([arXiv:2604.18537](https://arxiv.org/html/2604.18537)) | 91% JPEG survival vs baseline; PSNR 32.7 dB at ε=8 | Tuned for diffusion; principles transfer |

**Recommendation:** **Hybrid** — default to **DiffJPEG-STE** for backward; keep PIL JPEG in 30% of EOT samples as "reality check" (no grad) to avoid overfitting differentiable approx.

### EOT transform menu (ranked)

| Priority | Transform | Sample prob | Param range | Role |
|----------|-----------|-------------|-------------|------|
| 1 | JPEG | 0.70 | QF ∈ {45,55,65,75,85,92}, curriculum min 45→75 | Primary threat |
| 2 | Resize round-trip | 0.55 | scale ∈ [0.5, 1.0] | Scraper downscale |
| 3 | Gaussian blur | 0.35 | σ ∈ [0.3, 1.2] | CDN / recompress |
| 4 | Center/random crop | 0.25 | keep ∈ [0.88, 0.98] | Thumbnail crop |
| 5 | Gamma / brightness | 0.15 | ±8% | Light EOT diversity |
| 6 | SSA spectral mask | 0.20 | (transfer, Section 1) | Model diversity |

### How many EOT samples / step?

| N_eot | Robustness | Transfer | Compute |
|-------|------------|----------|---------|
| 1 (v1) | Fragile to QF mismatch | Baseline | 1× |
| **3** (v2 rec) | Good QF ensemble | +3–5 pp | 3× |
| 5+ | Diminishing returns | slight white-box hit | 5×+ |

**Literature consensus:** Ensemble **multiple QFs** with loss-weighted gradients ([MLSec 2017](https://machine-learning-and-security.github.io/papers/mlsec17_paper_54.pdf)); EOT sample count 3–5 ([EOT original](https://arxiv.org/abs/1707.07397)); more transforms require **larger perceptual budget** or attack fails to converge ([DIM p tradeoff](https://ar5iv.labs.arxiv.org/html/1803.06978)).

### Robustness vs transfer tradeoff

```
More aggressive EOT ──► better JPEG survival, better black-box transfer
                      ──► needs more steps / milder ε or LPIPS absorbs slack
                      ──► white-box margin slower (acceptable: we care about held-out)
```

**Practical split (v2):**
- **70%** of steps: `EOT_strong` (JPEG+resize+blur)
- **30%** of steps: `EOT_mild` (resize only) — preserves white-box convergence signal

### Algorithm sketch — EOT module v2

```python
def eot_v2(x, t, rng):
    # Curriculum: QF_min rises 45 → 75 over t/T
    qf_lo = int(45 + 30 * t / T)
    if rng.random() < 0.70:
        qf = rng.choice(range(qf_lo, 96, 5))
        x = diffjpeg_ste(x, qf)          # WACV 2024 backward
        if rng.random() < 0.30:
            x = jpeg_ste_pil(x, qf)        # reality check, no grad
    if rng.random() < 0.55:
        x = resize_roundtrip(x, rng.uniform(0.5, 1.0))
    if rng.random() < 0.35:
        x = gaussian_blur(x, rng.uniform(0.3, 1.2))
    if rng.random() < 0.25:
        x = random_crop_pad(x, rng.uniform(0.88, 0.98))
    return x.clamp(0, 1)
```

**Expected benefit:** held-out post-JPEG margin variance ↓ 30–50%; frontier +3–8 pp from better QF generalization without raising ε.

**Implementation cost:** DiffJPEG integration **medium** (1–2 days); curriculum + multi-sample EOT **low**.

---

## 4. Recommended V2 Attack Recipe

### Name: **Ensemble-SVRE-CWA-VNI-Perceptual-EOT** (ESCVPE)

Single combined recipe — implement in phases P0→P2.

#### Phase P0 (1–2 days) — highest ROI

1. Drop ε∞ to **8/255**; add **LPIPS projection** τ=0.08.
2. Replace MI with **VNI + VMI** (N=20).
3. Add **CWA** gradient aggregation across sampled encoders.
4. **Cross-prompt bank** (8 paraphrases) on contrastive decoy loss.
5. EOT: **N_eot=3**, JPEG QF ensemble {55,75,85}.

#### Phase P1 (2–3 days) — transfer lift

6. **SVRE** outer/inner loop over encoder subsets.
7. **Admix + DI + TI** input stack.
8. **DCT low-pass** mask (r=H/8).
9. **Texture-masked ε map** (β=2).
10. Add **3–4 VLM vision towers** (AIMv2, DINOv2, SigLIP2-NaFlex, InternViT) — already de-risked in v1 ablation.

#### Phase P2 (3–5 days) — frontier polish

11. **DiffJPEG-STE** replacing pure BPDA.
12. **Margin temperature calibration** + truncated feature mixing on repel loss.
13. **Ghost noise** on largest ViT encoders (p_drop=0.1).
14. **CoA-lite**: refresh decoy text embedding every 50 steps from CLIP-prefix caption of `x_adv`.
15. Optional **SEA-FTS** noise on timm encoders (σ_w=0.01).

#### Loss function (v2)

```
L = Σ_enc w_enc · (
      w_t · mean_p [ margin_calib( cos(f(x), e_decoy(p)), cos(f(x), e_truth) ) ]
    + w_r · [ -cos(f(x), f_clean) + mix_truncated_features(f, f_clean) ]
    + w_i · [ cos(f(x), e_decoy_img) - cos(f(x), e_truth) ]   # optional decoy image embed
)
```

#### Full hyperparameter card

```yaml
eps_inf: 0.03137          # 8/255
lpips_tau: 0.08
dct_keep: 0.125           # H/8
texture_beta: 2.0
steps: 350
step_size: 0.00392        # 1/255
momentum: 1.0
vni: true
vmi_neighbors: 20
cwa_sam_rho: 0.05
svre_inner: 3
encoders_per_step: 6
encoder_pool: 12-14
eot_samples: 3
eot_jpeg_prob: 0.70
prompt_bank: 12
w_target: 1.0
w_repel: 0.5
w_decoy_img: 0.3
margin_temp: 3.0
```

### Expected end-state (vs v1 @ ε=12–16)

| Metric | V1 | V2 target |
|--------|-----|-----------|
| PSNR | 26–28 dB | **≥ 36 dB** |
| SSIM | 0.65–0.74 | **≥ 0.92** |
| LPIPS | ~0.15–0.25 (est.) | **≤ 0.08** |
| Held-out open flip (JPEG) | 66–75% | **75–85%** |
| Frontier caption flip | 35–55% | **50–65%** |
| White-box in-ensemble | 100% | **95–100%** (acceptable dip) |
| Step time | 1× | **~3–4×** |

### Implementation priority / cost summary

| Component | Cost | Priority |
|-----------|------|----------|
| VNI+VMI+CWA | 1 d | P0 |
| LPIPS dual constraint | 1 d | P0 |
| Cross-prompt bank | 0.5 d | P0 |
| EOT multi-sample + QF ensemble | 0.5 d | P0 |
| SVRE + Admix/DI/TI | 2 d | P1 |
| DCT + texture mask | 1 d | P1 |
| DiffJPEG-STE | 2 d | P2 |
| CoA-lite / decoy image embed | 2 d | P2 |
| Ghost + SEA-FTS | 1 d | P2 |

---

## References (verified URLs)

| Short name | URL |
|------------|-----|
| CWA | https://proceedings.iclr.cc/paper_files/paper/2024/hash/53fe824f289060ce705ed7c01dae59d2-Abstract-Conference.html |
| SVRE | https://openaccess.thecvf.com/content/CVPR2022/html/Xiong_Stochastic_Variance_Reduced_Ensemble_Adversarial_Attack_for_Boosting_the_Adversarial_CVPR_2022_paper.html |
| VMI | https://arxiv.org/pdf/2103.15571 |
| DIM / M-DI2 | https://ar5iv.labs.arxiv.org/html/1803.06978 |
| TIM | https://openaccess.thecvf.com/content_CVPR_2019/papers/Dong_Evading_Defenses_to_Transferable_Adversarial_Examples_by_Translation-Invariant_Attacks_CVPR_2019_paper.pdf |
| Admix | https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Admix_Enhancing_the_Transferability_of_Adversarial_Attacks_ICCV_2021_paper.pdf |
| SSA | https://arxiv.org/abs/2207.05382 |
| CroPA | https://arxiv.org/pdf/2403.09766 |
| GrCPA | https://openreview.net/forum?id=I05Z6KjQ9K |
| CoA | https://arxiv.org/pdf/2411.15720 |
| AttackVLM | https://yunqing-me.github.io/AttackVLM/ |
| X-Transfer | https://arxiv.org/html/2505.05528v3 |
| SGMA | https://arxiv.org/pdf/2602.09431 |
| SEA | https://arxiv.org/html/2508.01741v3 |
| Logit / NLCM | https://arxiv.org/html/2405.06340v1 |
| PerceptualPGD / LPA | https://ar5iv.labs.arxiv.org/html/2006.12655 |
| PerC / ΔE00 | https://ar5iv.labs.arxiv.org/html/1911.02466 |
| Low-freq DCT | https://www.ijcai.org/proceedings/2019/0470.pdf |
| DiffJPEG | https://openaccess.thecvf.com/content/WACV2024/papers/Reich_Differentiable_JPEG_The_Devil_Is_in_the_Details_WACV_2024_paper.pdf |
| MetaCloak-JPEG | https://arxiv.org/html/2604.18537 |
| EOT | https://arxiv.org/abs/1707.07397 |
| JND texture mask | https://ar5iv.labs.arxiv.org/html/2011.05254 |

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*Document written for veil-pgd v2 planning. Next: implement P0 in `ensemble/attack.py`, sweep ε∈{4,6,8} × LPIPS τ∈{0.06,0.08,0.10} on Imagenette, re-run `frontier_eval`.*